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<a href="#nested-classes">类</a> &#124;
<a href="#pub-methods">Public 成员函数</a> &#124;
<a href="#pri-types">Private 类型</a> &#124;
<a href="#pri-methods">Private 成员函数</a> &#124;
<a href="#pri-attribs">Private 属性</a> &#124;
<a href="classpcl_1_1_c_p_c_segmentation-members.html">所有成员列表</a>  </div>
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<div class="title">pcl::CPCSegmentation&lt; PointT &gt; 模板类 参考</div>  </div>
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<p>A segmentation algorithm partitioning a supervoxel graph. It uses planar cuts induced by local concavities for the recursive segmentation. Cuts are estimated using locally constrained directed RANSAC.  
 <a href="classpcl_1_1_c_p_c_segmentation.html#details">更多...</a></p>

<p><code>#include &lt;<a class="el" href="cpc__segmentation_8h_source.html">cpc_segmentation.h</a>&gt;</code></p>
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类 pcl::CPCSegmentation&lt; PointT &gt; 继承关系图:</div>
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<area href="classpcl_1_1_l_c_c_p_segmentation.html" title="A simple segmentation algorithm partitioning a supervoxel graph into groups of locally convex connect..." alt="pcl::LCCPSegmentation&lt; PointT &gt;" shape="rect" coords="0,0,202,24"/>
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类</h2></td></tr>
<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus.html">WeightedRandomSampleConsensus</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight"><b><a class="el" href="classpcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus.html" title="WeightedRandomSampleConsensus represents an implementation of the Directionally Weighted RANSAC algor...">WeightedRandomSampleConsensus</a></b> represents an implementation of the Directionally Weighted RANSAC algorithm, as described in: "Constrained Planar Cuts - Part Segmentation for Point Clouds", CVPR 2015, M. Schoeler, J. Papon, F. Wörgötter.  <a href="classpcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus.html#details">更多...</a><br /></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2">&#160;</td></tr>
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<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-methods"></a>
Public 成员函数</h2></td></tr>
<tr class="memitem:a0ff7ee11473d36cbb774f90de8064908"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_c_p_c_segmentation.html#a0ff7ee11473d36cbb774f90de8064908">segment</a> ()</td></tr>
<tr class="memdesc:a0ff7ee11473d36cbb774f90de8064908"><td class="mdescLeft">&#160;</td><td class="mdescRight">Merge supervoxels using cuts through local convexities. The input parameters are generated by using the <a class="el" href="classpcl_1_1_supervoxel_clustering.html">SupervoxelClustering</a> class. To retrieve the output use the <a class="el" href="classpcl_1_1_l_c_c_p_segmentation.html#a73b148531688cdb949bb9185e6ada8e9">relabelCloud</a> method.  <a href="classpcl_1_1_c_p_c_segmentation.html#a0ff7ee11473d36cbb774f90de8064908">更多...</a><br /></td></tr>
<tr class="separator:a0ff7ee11473d36cbb774f90de8064908"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a0fdcebc606820bc008e779230503da04"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_c_p_c_segmentation.html#a0fdcebc606820bc008e779230503da04">setCutting</a> (const uint32_t max_cuts=20, const uint32_t cutting_min_segments=0, const float cutting_min_score=0.16, const bool locally_constrained=true, const bool directed_cutting=true, const bool clean_cutting=false)</td></tr>
<tr class="memdesc:a0fdcebc606820bc008e779230503da04"><td class="mdescLeft">&#160;</td><td class="mdescRight">Determines if we want to use cutting planes  <a href="classpcl_1_1_c_p_c_segmentation.html#a0fdcebc606820bc008e779230503da04">更多...</a><br /></td></tr>
<tr class="separator:a0fdcebc606820bc008e779230503da04"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ae45407cfb0975dc3ab6bb1f77c6df512"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_c_p_c_segmentation.html#ae45407cfb0975dc3ab6bb1f77c6df512">setRANSACIterations</a> (const uint32_t ransac_iterations)</td></tr>
<tr class="memdesc:ae45407cfb0975dc3ab6bb1f77c6df512"><td class="mdescLeft">&#160;</td><td class="mdescRight">Set the number of iterations for the weighted RANSAC step (best cut estimations)  <a href="classpcl_1_1_c_p_c_segmentation.html#ae45407cfb0975dc3ab6bb1f77c6df512">更多...</a><br /></td></tr>
<tr class="separator:ae45407cfb0975dc3ab6bb1f77c6df512"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="inherit_header pub_methods_classpcl_1_1_l_c_c_p_segmentation"><td colspan="2" onclick="javascript:toggleInherit('pub_methods_classpcl_1_1_l_c_c_p_segmentation')"><img src="closed.png" alt="-"/>&#160;Public 成员函数 继承自 <a class="el" href="classpcl_1_1_l_c_c_p_segmentation.html">pcl::LCCPSegmentation&lt; PointT &gt;</a></td></tr>
<tr class="memitem:af5a6ac69bde329584570506c7c78b9af inherit pub_methods_classpcl_1_1_l_c_c_p_segmentation"><td class="memItemLeft" align="right" valign="top"><a id="af5a6ac69bde329584570506c7c78b9af"></a>
void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_l_c_c_p_segmentation.html#af5a6ac69bde329584570506c7c78b9af">reset</a> ()</td></tr>
<tr class="memdesc:af5a6ac69bde329584570506c7c78b9af inherit pub_methods_classpcl_1_1_l_c_c_p_segmentation"><td class="mdescLeft">&#160;</td><td class="mdescRight">Reset internal memory. <br  />
 <br /></td></tr>
<tr class="separator:af5a6ac69bde329584570506c7c78b9af inherit pub_methods_classpcl_1_1_l_c_c_p_segmentation"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a097a5b8996de53f1d955747eacbc8e8e inherit pub_methods_classpcl_1_1_l_c_c_p_segmentation"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_l_c_c_p_segmentation.html#a097a5b8996de53f1d955747eacbc8e8e">setInputSupervoxels</a> (const std::map&lt; uint32_t, typename <a class="el" href="classpcl_1_1_supervoxel.html">pcl::Supervoxel</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;::Ptr &gt; &amp;supervoxel_clusters_arg, const std::multimap&lt; uint32_t, uint32_t &gt; &amp;label_adjacency_arg)</td></tr>
<tr class="memdesc:a097a5b8996de53f1d955747eacbc8e8e inherit pub_methods_classpcl_1_1_l_c_c_p_segmentation"><td class="mdescLeft">&#160;</td><td class="mdescRight">Set the supervoxel clusters as well as the adjacency graph for the segmentation.Those parameters are generated by using the <a class="el" href="classpcl_1_1_supervoxel_clustering.html">SupervoxelClustering</a> class. To retrieve the output use the <a class="el" href="classpcl_1_1_l_c_c_p_segmentation.html#a8ef6896e6fabc91e26f7f9dfff757753">segment</a> method.  <a href="classpcl_1_1_l_c_c_p_segmentation.html#a097a5b8996de53f1d955747eacbc8e8e">更多...</a><br /></td></tr>
<tr class="separator:a097a5b8996de53f1d955747eacbc8e8e inherit pub_methods_classpcl_1_1_l_c_c_p_segmentation"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a8ef6896e6fabc91e26f7f9dfff757753 inherit pub_methods_classpcl_1_1_l_c_c_p_segmentation"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_l_c_c_p_segmentation.html#a8ef6896e6fabc91e26f7f9dfff757753">segment</a> ()</td></tr>
<tr class="memdesc:a8ef6896e6fabc91e26f7f9dfff757753 inherit pub_methods_classpcl_1_1_l_c_c_p_segmentation"><td class="mdescLeft">&#160;</td><td class="mdescRight">Merge supervoxels using local convexity. The input parameters are generated by using the <a class="el" href="classpcl_1_1_supervoxel_clustering.html">SupervoxelClustering</a> class. To retrieve the output use the <a class="el" href="classpcl_1_1_l_c_c_p_segmentation.html#a73b148531688cdb949bb9185e6ada8e9">relabelCloud</a> method.  <a href="classpcl_1_1_l_c_c_p_segmentation.html#a8ef6896e6fabc91e26f7f9dfff757753">更多...</a><br /></td></tr>
<tr class="separator:a8ef6896e6fabc91e26f7f9dfff757753 inherit pub_methods_classpcl_1_1_l_c_c_p_segmentation"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a73b148531688cdb949bb9185e6ada8e9 inherit pub_methods_classpcl_1_1_l_c_c_p_segmentation"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_l_c_c_p_segmentation.html#a73b148531688cdb949bb9185e6ada8e9">relabelCloud</a> (<a class="el" href="classpcl_1_1_point_cloud.html">pcl::PointCloud</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_l.html">pcl::PointXYZL</a> &gt; &amp;labeled_cloud_arg)</td></tr>
<tr class="memdesc:a73b148531688cdb949bb9185e6ada8e9 inherit pub_methods_classpcl_1_1_l_c_c_p_segmentation"><td class="mdescLeft">&#160;</td><td class="mdescRight">Relabels cloud with supervoxel labels with the computed segment labels. labeled_cloud_arg should be created using the getLabeledCloud method of the <a class="el" href="classpcl_1_1_supervoxel_clustering.html">SupervoxelClustering</a> class.  <a href="classpcl_1_1_l_c_c_p_segmentation.html#a73b148531688cdb949bb9185e6ada8e9">更多...</a><br /></td></tr>
<tr class="separator:a73b148531688cdb949bb9185e6ada8e9 inherit pub_methods_classpcl_1_1_l_c_c_p_segmentation"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ac07470079debf27fbd456e57995c4c2d inherit pub_methods_classpcl_1_1_l_c_c_p_segmentation"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_l_c_c_p_segmentation.html#ac07470079debf27fbd456e57995c4c2d">getSegmentToSupervoxelMap</a> (std::map&lt; uint32_t, std::set&lt; uint32_t &gt; &gt; &amp;segment_supervoxel_map_arg) const</td></tr>
<tr class="memdesc:ac07470079debf27fbd456e57995c4c2d inherit pub_methods_classpcl_1_1_l_c_c_p_segmentation"><td class="mdescLeft">&#160;</td><td class="mdescRight">Get map&lt;SegmentID, std::set&lt;SuperVoxel IDs&gt; &gt;  <a href="classpcl_1_1_l_c_c_p_segmentation.html#ac07470079debf27fbd456e57995c4c2d">更多...</a><br /></td></tr>
<tr class="separator:ac07470079debf27fbd456e57995c4c2d inherit pub_methods_classpcl_1_1_l_c_c_p_segmentation"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ac99912aeb217065259bff30d7d526a35 inherit pub_methods_classpcl_1_1_l_c_c_p_segmentation"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_l_c_c_p_segmentation.html#ac99912aeb217065259bff30d7d526a35">getSupervoxelToSegmentMap</a> (std::map&lt; uint32_t, uint32_t &gt; &amp;supervoxel_segment_map_arg) const</td></tr>
<tr class="memdesc:ac99912aeb217065259bff30d7d526a35 inherit pub_methods_classpcl_1_1_l_c_c_p_segmentation"><td class="mdescLeft">&#160;</td><td class="mdescRight">Get map&lt;Supervoxel_ID, Segment_ID&gt;  <a href="classpcl_1_1_l_c_c_p_segmentation.html#ac99912aeb217065259bff30d7d526a35">更多...</a><br /></td></tr>
<tr class="separator:ac99912aeb217065259bff30d7d526a35 inherit pub_methods_classpcl_1_1_l_c_c_p_segmentation"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:af5753af2e6779ce28ddc3ae6c137893d inherit pub_methods_classpcl_1_1_l_c_c_p_segmentation"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_l_c_c_p_segmentation.html#af5753af2e6779ce28ddc3ae6c137893d">getSegmentAdjacencyMap</a> (std::map&lt; uint32_t, std::set&lt; uint32_t &gt; &gt; &amp;segment_adjacency_map_arg)</td></tr>
<tr class="memdesc:af5753af2e6779ce28ddc3ae6c137893d inherit pub_methods_classpcl_1_1_l_c_c_p_segmentation"><td class="mdescLeft">&#160;</td><td class="mdescRight">Get map &lt;SegmentID, std::set&lt;Neighboring SegmentIDs&gt; &gt;  <a href="classpcl_1_1_l_c_c_p_segmentation.html#af5753af2e6779ce28ddc3ae6c137893d">更多...</a><br /></td></tr>
<tr class="separator:af5753af2e6779ce28ddc3ae6c137893d inherit pub_methods_classpcl_1_1_l_c_c_p_segmentation"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ac4d9e71e46c6be921687146b33e9deed inherit pub_methods_classpcl_1_1_l_c_c_p_segmentation"><td class="memItemLeft" align="right" valign="top">float&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_l_c_c_p_segmentation.html#ac4d9e71e46c6be921687146b33e9deed">getConcavityToleranceThreshold</a> () const</td></tr>
<tr class="memdesc:ac4d9e71e46c6be921687146b33e9deed inherit pub_methods_classpcl_1_1_l_c_c_p_segmentation"><td class="mdescLeft">&#160;</td><td class="mdescRight">Get normal threshold  <a href="classpcl_1_1_l_c_c_p_segmentation.html#ac4d9e71e46c6be921687146b33e9deed">更多...</a><br /></td></tr>
<tr class="separator:ac4d9e71e46c6be921687146b33e9deed inherit pub_methods_classpcl_1_1_l_c_c_p_segmentation"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:aed49f8015934b23055f2d307a8ee9f01 inherit pub_methods_classpcl_1_1_l_c_c_p_segmentation"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_l_c_c_p_segmentation.html#aed49f8015934b23055f2d307a8ee9f01">getSVAdjacencyList</a> (SupervoxelAdjacencyList &amp;adjacency_list_arg) const</td></tr>
<tr class="memdesc:aed49f8015934b23055f2d307a8ee9f01 inherit pub_methods_classpcl_1_1_l_c_c_p_segmentation"><td class="mdescLeft">&#160;</td><td class="mdescRight">Get the supervoxel adjacency graph with classified edges (boost::adjacency_list).  <a href="classpcl_1_1_l_c_c_p_segmentation.html#aed49f8015934b23055f2d307a8ee9f01">更多...</a><br /></td></tr>
<tr class="separator:aed49f8015934b23055f2d307a8ee9f01 inherit pub_methods_classpcl_1_1_l_c_c_p_segmentation"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:abc2b7be596c2785fb8664922bbca7177 inherit pub_methods_classpcl_1_1_l_c_c_p_segmentation"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_l_c_c_p_segmentation.html#abc2b7be596c2785fb8664922bbca7177">setConcavityToleranceThreshold</a> (float concavity_tolerance_threshold_arg)</td></tr>
<tr class="memdesc:abc2b7be596c2785fb8664922bbca7177 inherit pub_methods_classpcl_1_1_l_c_c_p_segmentation"><td class="mdescLeft">&#160;</td><td class="mdescRight">Set normal threshold  <a href="classpcl_1_1_l_c_c_p_segmentation.html#abc2b7be596c2785fb8664922bbca7177">更多...</a><br /></td></tr>
<tr class="separator:abc2b7be596c2785fb8664922bbca7177 inherit pub_methods_classpcl_1_1_l_c_c_p_segmentation"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a391f12ac00e266066391bbff3fd1d823 inherit pub_methods_classpcl_1_1_l_c_c_p_segmentation"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_l_c_c_p_segmentation.html#a391f12ac00e266066391bbff3fd1d823">setSmoothnessCheck</a> (bool use_smoothness_check_arg, float voxel_res_arg, float seed_res_arg, float smoothness_threshold_arg=0.1)</td></tr>
<tr class="memdesc:a391f12ac00e266066391bbff3fd1d823 inherit pub_methods_classpcl_1_1_l_c_c_p_segmentation"><td class="mdescLeft">&#160;</td><td class="mdescRight">Determines if a smoothness check is done during segmentation, trying to invalidate edges of non-smooth connected edges (steps). Two supervoxels are unsmooth if their plane-to-plane distance DIST &gt; (expected_distance + smoothness_threshold_*voxel_resolution_). For parallel supervoxels, the expected_distance is zero.  <a href="classpcl_1_1_l_c_c_p_segmentation.html#a391f12ac00e266066391bbff3fd1d823">更多...</a><br /></td></tr>
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<tr class="memitem:afcbcd533f01655310bec5f17c5213c65 inherit pub_methods_classpcl_1_1_l_c_c_p_segmentation"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_l_c_c_p_segmentation.html#afcbcd533f01655310bec5f17c5213c65">setSanityCheck</a> (const bool use_sanity_criterion_arg)</td></tr>
<tr class="memdesc:afcbcd533f01655310bec5f17c5213c65 inherit pub_methods_classpcl_1_1_l_c_c_p_segmentation"><td class="mdescLeft">&#160;</td><td class="mdescRight">Determines if we want to use the sanity criterion to invalidate singular connected patches  <a href="classpcl_1_1_l_c_c_p_segmentation.html#afcbcd533f01655310bec5f17c5213c65">更多...</a><br /></td></tr>
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<tr class="memitem:affa15262ca3f2713ee31412d88697d36 inherit pub_methods_classpcl_1_1_l_c_c_p_segmentation"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_l_c_c_p_segmentation.html#affa15262ca3f2713ee31412d88697d36">setKFactor</a> (const uint32_t k_factor_arg)</td></tr>
<tr class="memdesc:affa15262ca3f2713ee31412d88697d36 inherit pub_methods_classpcl_1_1_l_c_c_p_segmentation"><td class="mdescLeft">&#160;</td><td class="mdescRight">Set the value used for k convexity. For k&gt;0 convex connections between p_i and p_j require k common neighbors of these patches that have a convex connection to both.  <a href="classpcl_1_1_l_c_c_p_segmentation.html#affa15262ca3f2713ee31412d88697d36">更多...</a><br /></td></tr>
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<tr class="memitem:a700734f0f748fdda3110c7924232e9c9 inherit pub_methods_classpcl_1_1_l_c_c_p_segmentation"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_l_c_c_p_segmentation.html#a700734f0f748fdda3110c7924232e9c9">setMinSegmentSize</a> (const uint32_t min_segment_size_arg)</td></tr>
<tr class="memdesc:a700734f0f748fdda3110c7924232e9c9 inherit pub_methods_classpcl_1_1_l_c_c_p_segmentation"><td class="mdescLeft">&#160;</td><td class="mdescRight">Set the value <a class="el" href="classpcl_1_1_l_c_c_p_segmentation.html#a3cbd29b0450bed2d70257ab13bf5bf38">min_segment_size_</a> used in <a class="el" href="classpcl_1_1_l_c_c_p_segmentation.html#a7f0ada4d9a4918d9dbb9e33e32b23d46">mergeSmallSegments</a>  <a href="classpcl_1_1_l_c_c_p_segmentation.html#a700734f0f748fdda3110c7924232e9c9">更多...</a><br /></td></tr>
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Private 类型</h2></td></tr>
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typedef <a class="el" href="structpcl_1_1_point_x_y_z_i_normal.html">PointXYZINormal</a>&#160;</td><td class="memItemRight" valign="bottom"><b>WeightSACPointType</b></td></tr>
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<tr class="memitem:ade38668148d14bb0b1c1fb5ba05f45d6"><td class="memItemLeft" align="right" valign="top"><a id="ade38668148d14bb0b1c1fb5ba05f45d6"></a>
typedef <a class="el" href="classpcl_1_1_l_c_c_p_segmentation.html">LCCPSegmentation</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;&#160;</td><td class="memItemRight" valign="bottom"><b>LCCP</b></td></tr>
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typedef LCCP::EdgeID&#160;</td><td class="memItemRight" valign="bottom"><b>EdgeID</b></td></tr>
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typedef LCCP::EdgeIterator&#160;</td><td class="memItemRight" valign="bottom"><b>EdgeIterator</b></td></tr>
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Private 成员函数</h2></td></tr>
<tr class="memitem:aba7a4f7d9481b0c9c88edc6d301964d9"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_c_p_c_segmentation.html#aba7a4f7d9481b0c9c88edc6d301964d9">applyCuttingPlane</a> (uint32_t depth_levels_left)</td></tr>
<tr class="memdesc:aba7a4f7d9481b0c9c88edc6d301964d9"><td class="mdescLeft">&#160;</td><td class="mdescRight">Used in for CPC to find and fit cutting planes to the pointcloud.  <a href="classpcl_1_1_c_p_c_segmentation.html#aba7a4f7d9481b0c9c88edc6d301964d9">更多...</a><br /></td></tr>
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<tr class="memitem:a64ade0e74f07da2c8100a1a9d5d46e00"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_c_p_c_segmentation.html#a64ade0e74f07da2c8100a1a9d5d46e00">calculateConvexConnections</a> (SupervoxelAdjacencyList &amp;adjacency_list_arg)</td></tr>
<tr class="memdesc:a64ade0e74f07da2c8100a1a9d5d46e00"><td class="mdescLeft">&#160;</td><td class="mdescRight">Calculates convexity of edges and saves this to the adjacency graph.  <a href="classpcl_1_1_c_p_c_segmentation.html#a64ade0e74f07da2c8100a1a9d5d46e00">更多...</a><br /></td></tr>
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<tr class="memitem:ad918a280410d18af75bad10b3134e5ab"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_c_p_c_segmentation.html#ad918a280410d18af75bad10b3134e5ab">applyKconvexity</a> (const unsigned int k_arg)</td></tr>
<tr class="memdesc:ad918a280410d18af75bad10b3134e5ab"><td class="mdescLeft">&#160;</td><td class="mdescRight">Connections are only convex if this is true for at least k_arg common neighbors of the two patches. Call <a class="el" href="classpcl_1_1_l_c_c_p_segmentation.html#affa15262ca3f2713ee31412d88697d36">setKFactor</a> before <a class="el" href="classpcl_1_1_c_p_c_segmentation.html#a0ff7ee11473d36cbb774f90de8064908">segment</a> to use this.  <a href="classpcl_1_1_c_p_c_segmentation.html#ad918a280410d18af75bad10b3134e5ab">更多...</a><br /></td></tr>
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<tr class="memitem:ad1a810ce20594b9a9309c29f089f0d18"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_c_p_c_segmentation.html#ad1a810ce20594b9a9309c29f089f0d18">doGrouping</a> ()</td></tr>
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<tr class="memitem:a7f0ada4d9a4918d9dbb9e33e32b23d46"><td class="memItemLeft" align="right" valign="top"><a id="a7f0ada4d9a4918d9dbb9e33e32b23d46"></a>
void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_c_p_c_segmentation.html#a7f0ada4d9a4918d9dbb9e33e32b23d46">mergeSmallSegments</a> ()</td></tr>
<tr class="memdesc:a7f0ada4d9a4918d9dbb9e33e32b23d46"><td class="mdescLeft">&#160;</td><td class="mdescRight">Segments smaller than <a class="el" href="classpcl_1_1_l_c_c_p_segmentation.html#a3cbd29b0450bed2d70257ab13bf5bf38">min_segment_size_</a> are merged to the label of largest neighbor <br /></td></tr>
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<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pri-attribs"></a>
Private 属性</h2></td></tr>
<tr class="memitem:a6293e15b7d22fbb19d7819bedba683f1"><td class="memItemLeft" align="right" valign="top">uint32_t&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_c_p_c_segmentation.html#a6293e15b7d22fbb19d7819bedba683f1">max_cuts_</a></td></tr>
<tr class="memdesc:a6293e15b7d22fbb19d7819bedba683f1"><td class="mdescLeft">&#160;</td><td class="mdescRight">*** Parameters *** ///  <a href="classpcl_1_1_c_p_c_segmentation.html#a6293e15b7d22fbb19d7819bedba683f1">更多...</a><br /></td></tr>
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<tr class="memitem:a6eda4c6ab0c6d0f55b11c5a666accd7f"><td class="memItemLeft" align="right" valign="top"><a id="a6eda4c6ab0c6d0f55b11c5a666accd7f"></a>
uint32_t&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_c_p_c_segmentation.html#a6eda4c6ab0c6d0f55b11c5a666accd7f">min_segment_size_for_cutting_</a></td></tr>
<tr class="memdesc:a6eda4c6ab0c6d0f55b11c5a666accd7f"><td class="mdescLeft">&#160;</td><td class="mdescRight">Minimum segment size for cutting <br /></td></tr>
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float&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_c_p_c_segmentation.html#ac04198491da197fdbb9478dbf682f1ec">min_cut_score_</a></td></tr>
<tr class="memdesc:ac04198491da197fdbb9478dbf682f1ec"><td class="mdescLeft">&#160;</td><td class="mdescRight">Cut_score threshold <br /></td></tr>
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bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_c_p_c_segmentation.html#a6016df56b09c1f9b13db954e1805d930">use_local_constrains_</a></td></tr>
<tr class="memdesc:a6016df56b09c1f9b13db954e1805d930"><td class="mdescLeft">&#160;</td><td class="mdescRight">Use local constrains for cutting <br /></td></tr>
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bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_c_p_c_segmentation.html#aceaa9f8130856b9304830674ac7515c8">use_directed_weights_</a></td></tr>
<tr class="memdesc:aceaa9f8130856b9304830674ac7515c8"><td class="mdescLeft">&#160;</td><td class="mdescRight">Use directed weights for the cutting <br /></td></tr>
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<tr class="memitem:a743b8b7bfcb33d9edfcbd9fa1ecc2eac"><td class="memItemLeft" align="right" valign="top"><a id="a743b8b7bfcb33d9edfcbd9fa1ecc2eac"></a>
bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_c_p_c_segmentation.html#a743b8b7bfcb33d9edfcbd9fa1ecc2eac">use_clean_cutting_</a></td></tr>
<tr class="memdesc:a743b8b7bfcb33d9edfcbd9fa1ecc2eac"><td class="mdescLeft">&#160;</td><td class="mdescRight">Use clean cutting <br /></td></tr>
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<tr class="memitem:aa1eac80686a308fdd04592bdefd9e6bd"><td class="memItemLeft" align="right" valign="top"><a id="aa1eac80686a308fdd04592bdefd9e6bd"></a>
uint32_t&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_c_p_c_segmentation.html#aa1eac80686a308fdd04592bdefd9e6bd">ransac_itrs_</a></td></tr>
<tr class="memdesc:aa1eac80686a308fdd04592bdefd9e6bd"><td class="mdescLeft">&#160;</td><td class="mdescRight">Interations for RANSAC <br /></td></tr>
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<tr class="memitem:adff367bb7eab2ec652da194f36ad2ab4"><td class="memItemLeft" align="right" valign="top"><a id="adff367bb7eab2ec652da194f36ad2ab4"></a>
SupervoxelAdjacencyList&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_c_p_c_segmentation.html#adff367bb7eab2ec652da194f36ad2ab4">sv_adjacency_list_</a></td></tr>
<tr class="memdesc:adff367bb7eab2ec652da194f36ad2ab4"><td class="mdescLeft">&#160;</td><td class="mdescRight">Adjacency graph with the supervoxel labels as nodes and edges between adjacent supervoxels <br /></td></tr>
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uint32_t&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_c_p_c_segmentation.html#ab56b15cb177706d688e6773368e123e2">k_factor_</a></td></tr>
<tr class="memdesc:ab56b15cb177706d688e6773368e123e2"><td class="mdescLeft">&#160;</td><td class="mdescRight">Factor used for k-convexity <br /></td></tr>
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<tr class="memitem:a428e19cb5f6711c7d2e20f31472a876a"><td class="memItemLeft" align="right" valign="top"><a id="a428e19cb5f6711c7d2e20f31472a876a"></a>
bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_c_p_c_segmentation.html#a428e19cb5f6711c7d2e20f31472a876a">grouping_data_valid_</a></td></tr>
<tr class="memdesc:a428e19cb5f6711c7d2e20f31472a876a"><td class="mdescLeft">&#160;</td><td class="mdescRight">Marks if valid grouping data (<a class="el" href="classpcl_1_1_c_p_c_segmentation.html#adff367bb7eab2ec652da194f36ad2ab4">sv_adjacency_list_</a>, <a class="el" href="classpcl_1_1_c_p_c_segmentation.html#afb6ff37d270e3f16c69c46560a1fafce">sv_label_to_seg_label_map_</a>, <a class="el" href="classpcl_1_1_l_c_c_p_segmentation.html#a40779a978d7208a82cc9421c4033a1e4">processed_</a>) is avaiable <br /></td></tr>
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<tr class="memitem:afb6ff37d270e3f16c69c46560a1fafce"><td class="memItemLeft" align="right" valign="top">std::map&lt; uint32_t, uint32_t &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_c_p_c_segmentation.html#afb6ff37d270e3f16c69c46560a1fafce">sv_label_to_seg_label_map_</a></td></tr>
<tr class="memdesc:afb6ff37d270e3f16c69c46560a1fafce"><td class="mdescLeft">&#160;</td><td class="mdescRight">Storing relation between original SuperVoxel Labels and new segmantion labels.  <a href="classpcl_1_1_c_p_c_segmentation.html#afb6ff37d270e3f16c69c46560a1fafce">更多...</a><br /></td></tr>
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<tr class="memitem:a6e8c0fd169543d42903904b02d36239b"><td class="memItemLeft" align="right" valign="top"><a id="a6e8c0fd169543d42903904b02d36239b"></a>
std::map&lt; uint32_t, typename <a class="el" href="classpcl_1_1_supervoxel.html">pcl::Supervoxel</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;::Ptr &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_c_p_c_segmentation.html#a6e8c0fd169543d42903904b02d36239b">sv_label_to_supervoxel_map_</a></td></tr>
<tr class="memdesc:a6e8c0fd169543d42903904b02d36239b"><td class="mdescLeft">&#160;</td><td class="mdescRight">map from the supervoxel labels to the supervoxel objects <br  />
 <br /></td></tr>
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<tr class="memitem:a90f2ad90bee047f31f2c9ad4f3b0c158"><td class="memItemLeft" align="right" valign="top">float&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_c_p_c_segmentation.html#a90f2ad90bee047f31f2c9ad4f3b0c158">concavity_tolerance_threshold_</a></td></tr>
<tr class="memdesc:a90f2ad90bee047f31f2c9ad4f3b0c158"><td class="mdescLeft">&#160;</td><td class="mdescRight">*** Parameters *** ///  <a href="classpcl_1_1_c_p_c_segmentation.html#a90f2ad90bee047f31f2c9ad4f3b0c158">更多...</a><br /></td></tr>
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float&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_c_p_c_segmentation.html#aa9f7011e99af9d3849937ff5370c2e11">seed_resolution_</a></td></tr>
<tr class="memdesc:aa9f7011e99af9d3849937ff5370c2e11"><td class="mdescLeft">&#160;</td><td class="mdescRight">Seed resolution of the supervoxels (used only for smoothness check) <br /></td></tr>
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<tr class="memitem:a0ebcf3b12da8ec8ff9029a4bc77292b6"><td class="memItemLeft" align="right" valign="top"><a id="a0ebcf3b12da8ec8ff9029a4bc77292b6"></a>
bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_c_p_c_segmentation.html#a0ebcf3b12da8ec8ff9029a4bc77292b6">supervoxels_set_</a></td></tr>
<tr class="memdesc:a0ebcf3b12da8ec8ff9029a4bc77292b6"><td class="mdescLeft">&#160;</td><td class="mdescRight">Marks if supervoxels have been set by calling <a class="el" href="classpcl_1_1_l_c_c_p_segmentation.html#a097a5b8996de53f1d955747eacbc8e8e">setInputSupervoxels</a> <br /></td></tr>
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额外继承的成员函数</h2></td></tr>
<tr class="inherit_header pub_types_classpcl_1_1_l_c_c_p_segmentation"><td colspan="2" onclick="javascript:toggleInherit('pub_types_classpcl_1_1_l_c_c_p_segmentation')"><img src="closed.png" alt="-"/>&#160;Public 类型 继承自 <a class="el" href="classpcl_1_1_l_c_c_p_segmentation.html">pcl::LCCPSegmentation&lt; PointT &gt;</a></td></tr>
<tr class="memitem:a1c2eb66ea772fab4b9cc938bfb124e3b inherit pub_types_classpcl_1_1_l_c_c_p_segmentation"><td class="memItemLeft" align="right" valign="top"><a id="a1c2eb66ea772fab4b9cc938bfb124e3b"></a>
typedef boost::adjacency_list&lt; boost::setS, boost::setS, boost::undirectedS, uint32_t, <a class="el" href="structpcl_1_1_l_c_c_p_segmentation_1_1_edge_properties.html">EdgeProperties</a> &gt;&#160;</td><td class="memItemRight" valign="bottom"><b>SupervoxelAdjacencyList</b></td></tr>
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typedef boost::graph_traits&lt; SupervoxelAdjacencyList &gt;::vertex_iterator&#160;</td><td class="memItemRight" valign="bottom"><b>VertexIterator</b></td></tr>
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typedef boost::graph_traits&lt; SupervoxelAdjacencyList &gt;::adjacency_iterator&#160;</td><td class="memItemRight" valign="bottom"><b>AdjacencyIterator</b></td></tr>
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typedef boost::graph_traits&lt; SupervoxelAdjacencyList &gt;::vertex_descriptor&#160;</td><td class="memItemRight" valign="bottom"><b>VertexID</b></td></tr>
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typedef boost::graph_traits&lt; SupervoxelAdjacencyList &gt;::edge_iterator&#160;</td><td class="memItemRight" valign="bottom"><b>EdgeIterator</b></td></tr>
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typedef boost::graph_traits&lt; SupervoxelAdjacencyList &gt;::out_edge_iterator&#160;</td><td class="memItemRight" valign="bottom"><b>OutEdgeIterator</b></td></tr>
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<tr class="memitem:ac5b56b02f769a92c815ff98ee63b497e inherit pub_types_classpcl_1_1_l_c_c_p_segmentation"><td class="memItemLeft" align="right" valign="top"><a id="ac5b56b02f769a92c815ff98ee63b497e"></a>
typedef boost::graph_traits&lt; SupervoxelAdjacencyList &gt;::edge_descriptor&#160;</td><td class="memItemRight" valign="bottom"><b>EdgeID</b></td></tr>
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<tr class="inherit_header pro_methods_classpcl_1_1_l_c_c_p_segmentation"><td colspan="2" onclick="javascript:toggleInherit('pro_methods_classpcl_1_1_l_c_c_p_segmentation')"><img src="closed.png" alt="-"/>&#160;Protected 成员函数 继承自 <a class="el" href="classpcl_1_1_l_c_c_p_segmentation.html">pcl::LCCPSegmentation&lt; PointT &gt;</a></td></tr>
<tr class="memitem:a7f0ada4d9a4918d9dbb9e33e32b23d46 inherit pro_methods_classpcl_1_1_l_c_c_p_segmentation"><td class="memItemLeft" align="right" valign="top"><a id="a7f0ada4d9a4918d9dbb9e33e32b23d46"></a>
void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_l_c_c_p_segmentation.html#a7f0ada4d9a4918d9dbb9e33e32b23d46">mergeSmallSegments</a> ()</td></tr>
<tr class="memdesc:a7f0ada4d9a4918d9dbb9e33e32b23d46 inherit pro_methods_classpcl_1_1_l_c_c_p_segmentation"><td class="mdescLeft">&#160;</td><td class="mdescRight">Segments smaller than <a class="el" href="classpcl_1_1_l_c_c_p_segmentation.html#a3cbd29b0450bed2d70257ab13bf5bf38">min_segment_size_</a> are merged to the label of largest neighbor <br /></td></tr>
<tr class="separator:a7f0ada4d9a4918d9dbb9e33e32b23d46 inherit pro_methods_classpcl_1_1_l_c_c_p_segmentation"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a6a3d6ce5b725f4ba8989654bcb94c1e5 inherit pro_methods_classpcl_1_1_l_c_c_p_segmentation"><td class="memItemLeft" align="right" valign="top"><a id="a6a3d6ce5b725f4ba8989654bcb94c1e5"></a>
void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_l_c_c_p_segmentation.html#a6a3d6ce5b725f4ba8989654bcb94c1e5">computeSegmentAdjacency</a> ()</td></tr>
<tr class="memdesc:a6a3d6ce5b725f4ba8989654bcb94c1e5 inherit pro_methods_classpcl_1_1_l_c_c_p_segmentation"><td class="mdescLeft">&#160;</td><td class="mdescRight">Compute the adjacency of the segments <br /></td></tr>
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<tr class="memitem:a6336316795782b93b5caef627f2c4051 inherit pro_methods_classpcl_1_1_l_c_c_p_segmentation"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_l_c_c_p_segmentation.html#a6336316795782b93b5caef627f2c4051">prepareSegmentation</a> (const std::map&lt; uint32_t, typename <a class="el" href="classpcl_1_1_supervoxel.html">pcl::Supervoxel</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;::Ptr &gt; &amp;supervoxel_clusters_arg, const std::multimap&lt; uint32_t, uint32_t &gt; &amp;label_adjacency_arg)</td></tr>
<tr class="memdesc:a6336316795782b93b5caef627f2c4051 inherit pro_methods_classpcl_1_1_l_c_c_p_segmentation"><td class="mdescLeft">&#160;</td><td class="mdescRight">Is called within <a class="el" href="classpcl_1_1_l_c_c_p_segmentation.html#a097a5b8996de53f1d955747eacbc8e8e">setInputSupervoxels</a> mainly to reserve required memory.  <a href="classpcl_1_1_l_c_c_p_segmentation.html#a6336316795782b93b5caef627f2c4051">更多...</a><br /></td></tr>
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<tr class="memitem:ad1a810ce20594b9a9309c29f089f0d18 inherit pro_methods_classpcl_1_1_l_c_c_p_segmentation"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_l_c_c_p_segmentation.html#ad1a810ce20594b9a9309c29f089f0d18">doGrouping</a> ()</td></tr>
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<tr class="memitem:a4a492a4362ba841361155407ef78c00e inherit pro_methods_classpcl_1_1_l_c_c_p_segmentation"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_l_c_c_p_segmentation.html#a4a492a4362ba841361155407ef78c00e">recursiveSegmentGrowing</a> (const VertexID &amp;queryPointID, const unsigned int group_label)</td></tr>
<tr class="memdesc:a4a492a4362ba841361155407ef78c00e inherit pro_methods_classpcl_1_1_l_c_c_p_segmentation"><td class="mdescLeft">&#160;</td><td class="mdescRight">Assigns neighbors of the query point to the same group as the query point. Recursive part of <a class="el" href="classpcl_1_1_l_c_c_p_segmentation.html#ad1a810ce20594b9a9309c29f089f0d18">doGrouping</a>. Grouping is done by a depth-search of nodes in the adjacency-graph.  <a href="classpcl_1_1_l_c_c_p_segmentation.html#a4a492a4362ba841361155407ef78c00e">更多...</a><br /></td></tr>
<tr class="separator:a4a492a4362ba841361155407ef78c00e inherit pro_methods_classpcl_1_1_l_c_c_p_segmentation"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a64ade0e74f07da2c8100a1a9d5d46e00 inherit pro_methods_classpcl_1_1_l_c_c_p_segmentation"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_l_c_c_p_segmentation.html#a64ade0e74f07da2c8100a1a9d5d46e00">calculateConvexConnections</a> (SupervoxelAdjacencyList &amp;adjacency_list_arg)</td></tr>
<tr class="memdesc:a64ade0e74f07da2c8100a1a9d5d46e00 inherit pro_methods_classpcl_1_1_l_c_c_p_segmentation"><td class="mdescLeft">&#160;</td><td class="mdescRight">Calculates convexity of edges and saves this to the adjacency graph.  <a href="classpcl_1_1_l_c_c_p_segmentation.html#a64ade0e74f07da2c8100a1a9d5d46e00">更多...</a><br /></td></tr>
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<tr class="memitem:ad918a280410d18af75bad10b3134e5ab inherit pro_methods_classpcl_1_1_l_c_c_p_segmentation"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_l_c_c_p_segmentation.html#ad918a280410d18af75bad10b3134e5ab">applyKconvexity</a> (const unsigned int k_arg)</td></tr>
<tr class="memdesc:ad918a280410d18af75bad10b3134e5ab inherit pro_methods_classpcl_1_1_l_c_c_p_segmentation"><td class="mdescLeft">&#160;</td><td class="mdescRight">Connections are only convex if this is true for at least k_arg common neighbors of the two patches. Call <a class="el" href="classpcl_1_1_l_c_c_p_segmentation.html#affa15262ca3f2713ee31412d88697d36">setKFactor</a> before <a class="el" href="classpcl_1_1_l_c_c_p_segmentation.html#a8ef6896e6fabc91e26f7f9dfff757753">segment</a> to use this.  <a href="classpcl_1_1_l_c_c_p_segmentation.html#ad918a280410d18af75bad10b3134e5ab">更多...</a><br /></td></tr>
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<tr class="memitem:ab7189d3e7ad6bcb44b995664c6a0b77e inherit pro_methods_classpcl_1_1_l_c_c_p_segmentation"><td class="memItemLeft" align="right" valign="top">bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_l_c_c_p_segmentation.html#ab7189d3e7ad6bcb44b995664c6a0b77e">connIsConvex</a> (const uint32_t source_label_arg, const uint32_t target_label_arg, float &amp;normal_angle)</td></tr>
<tr class="memdesc:ab7189d3e7ad6bcb44b995664c6a0b77e inherit pro_methods_classpcl_1_1_l_c_c_p_segmentation"><td class="mdescLeft">&#160;</td><td class="mdescRight">Returns true if the connection between source and target is convex.  <a href="classpcl_1_1_l_c_c_p_segmentation.html#ab7189d3e7ad6bcb44b995664c6a0b77e">更多...</a><br /></td></tr>
<tr class="separator:ab7189d3e7ad6bcb44b995664c6a0b77e inherit pro_methods_classpcl_1_1_l_c_c_p_segmentation"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="inherit_header pro_attribs_classpcl_1_1_l_c_c_p_segmentation"><td colspan="2" onclick="javascript:toggleInherit('pro_attribs_classpcl_1_1_l_c_c_p_segmentation')"><img src="closed.png" alt="-"/>&#160;Protected 属性 继承自 <a class="el" href="classpcl_1_1_l_c_c_p_segmentation.html">pcl::LCCPSegmentation&lt; PointT &gt;</a></td></tr>
<tr class="memitem:a90f2ad90bee047f31f2c9ad4f3b0c158 inherit pro_attribs_classpcl_1_1_l_c_c_p_segmentation"><td class="memItemLeft" align="right" valign="top">float&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_l_c_c_p_segmentation.html#a90f2ad90bee047f31f2c9ad4f3b0c158">concavity_tolerance_threshold_</a></td></tr>
<tr class="memdesc:a90f2ad90bee047f31f2c9ad4f3b0c158 inherit pro_attribs_classpcl_1_1_l_c_c_p_segmentation"><td class="mdescLeft">&#160;</td><td class="mdescRight">*** Parameters *** ///  <a href="classpcl_1_1_l_c_c_p_segmentation.html#a90f2ad90bee047f31f2c9ad4f3b0c158">更多...</a><br /></td></tr>
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<tr class="memitem:a428e19cb5f6711c7d2e20f31472a876a inherit pro_attribs_classpcl_1_1_l_c_c_p_segmentation"><td class="memItemLeft" align="right" valign="top"><a id="a428e19cb5f6711c7d2e20f31472a876a"></a>
bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_l_c_c_p_segmentation.html#a428e19cb5f6711c7d2e20f31472a876a">grouping_data_valid_</a></td></tr>
<tr class="memdesc:a428e19cb5f6711c7d2e20f31472a876a inherit pro_attribs_classpcl_1_1_l_c_c_p_segmentation"><td class="mdescLeft">&#160;</td><td class="mdescRight">Marks if valid grouping data (<a class="el" href="classpcl_1_1_l_c_c_p_segmentation.html#adff367bb7eab2ec652da194f36ad2ab4">sv_adjacency_list_</a>, <a class="el" href="classpcl_1_1_l_c_c_p_segmentation.html#afb6ff37d270e3f16c69c46560a1fafce">sv_label_to_seg_label_map_</a>, <a class="el" href="classpcl_1_1_l_c_c_p_segmentation.html#a40779a978d7208a82cc9421c4033a1e4">processed_</a>) is avaiable <br /></td></tr>
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<tr class="memitem:a0ebcf3b12da8ec8ff9029a4bc77292b6 inherit pro_attribs_classpcl_1_1_l_c_c_p_segmentation"><td class="memItemLeft" align="right" valign="top"><a id="a0ebcf3b12da8ec8ff9029a4bc77292b6"></a>
bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_l_c_c_p_segmentation.html#a0ebcf3b12da8ec8ff9029a4bc77292b6">supervoxels_set_</a></td></tr>
<tr class="memdesc:a0ebcf3b12da8ec8ff9029a4bc77292b6 inherit pro_attribs_classpcl_1_1_l_c_c_p_segmentation"><td class="mdescLeft">&#160;</td><td class="mdescRight">Marks if supervoxels have been set by calling <a class="el" href="classpcl_1_1_l_c_c_p_segmentation.html#a097a5b8996de53f1d955747eacbc8e8e">setInputSupervoxels</a> <br /></td></tr>
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<tr class="memitem:a6c58a7748116a2fa0e9d820ffadb718a inherit pro_attribs_classpcl_1_1_l_c_c_p_segmentation"><td class="memItemLeft" align="right" valign="top"><a id="a6c58a7748116a2fa0e9d820ffadb718a"></a>
bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_l_c_c_p_segmentation.html#a6c58a7748116a2fa0e9d820ffadb718a">use_smoothness_check_</a></td></tr>
<tr class="memdesc:a6c58a7748116a2fa0e9d820ffadb718a inherit pro_attribs_classpcl_1_1_l_c_c_p_segmentation"><td class="mdescLeft">&#160;</td><td class="mdescRight">Determines if the smoothness check is used during segmentation <br /></td></tr>
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<tr class="memitem:aa4b03eeae0ef4422a5e49b41455a098d inherit pro_attribs_classpcl_1_1_l_c_c_p_segmentation"><td class="memItemLeft" align="right" valign="top"><a id="aa4b03eeae0ef4422a5e49b41455a098d"></a>
float&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_l_c_c_p_segmentation.html#aa4b03eeae0ef4422a5e49b41455a098d">smoothness_threshold_</a></td></tr>
<tr class="memdesc:aa4b03eeae0ef4422a5e49b41455a098d inherit pro_attribs_classpcl_1_1_l_c_c_p_segmentation"><td class="mdescLeft">&#160;</td><td class="mdescRight">Two supervoxels are unsmooth if their plane-to-plane distance DIST &gt; (expected_distance + smoothness_threshold_*voxel_resolution_). For parallel supervoxels, the expected_distance is zero. <br /></td></tr>
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<tr class="memitem:a728844e11f8848c7400d7875bae92800 inherit pro_attribs_classpcl_1_1_l_c_c_p_segmentation"><td class="memItemLeft" align="right" valign="top"><a id="a728844e11f8848c7400d7875bae92800"></a>
bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_l_c_c_p_segmentation.html#a728844e11f8848c7400d7875bae92800">use_sanity_check_</a></td></tr>
<tr class="memdesc:a728844e11f8848c7400d7875bae92800 inherit pro_attribs_classpcl_1_1_l_c_c_p_segmentation"><td class="mdescLeft">&#160;</td><td class="mdescRight">Determines if we use the sanity check which tries to find and invalidate singular connected patches <br /></td></tr>
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float&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_l_c_c_p_segmentation.html#aa9f7011e99af9d3849937ff5370c2e11">seed_resolution_</a></td></tr>
<tr class="memdesc:aa9f7011e99af9d3849937ff5370c2e11 inherit pro_attribs_classpcl_1_1_l_c_c_p_segmentation"><td class="mdescLeft">&#160;</td><td class="mdescRight">Seed resolution of the supervoxels (used only for smoothness check) <br /></td></tr>
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float&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_l_c_c_p_segmentation.html#a0998d8eabad97e8dbc57973b28d4b389">voxel_resolution_</a></td></tr>
<tr class="memdesc:a0998d8eabad97e8dbc57973b28d4b389 inherit pro_attribs_classpcl_1_1_l_c_c_p_segmentation"><td class="mdescLeft">&#160;</td><td class="mdescRight">Voxel resolution used to build the supervoxels (used only for smoothness check) <br /></td></tr>
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uint32_t&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_l_c_c_p_segmentation.html#ab56b15cb177706d688e6773368e123e2">k_factor_</a></td></tr>
<tr class="memdesc:ab56b15cb177706d688e6773368e123e2 inherit pro_attribs_classpcl_1_1_l_c_c_p_segmentation"><td class="mdescLeft">&#160;</td><td class="mdescRight">Factor used for k-convexity <br /></td></tr>
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uint32_t&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_l_c_c_p_segmentation.html#a3cbd29b0450bed2d70257ab13bf5bf38">min_segment_size_</a></td></tr>
<tr class="memdesc:a3cbd29b0450bed2d70257ab13bf5bf38 inherit pro_attribs_classpcl_1_1_l_c_c_p_segmentation"><td class="mdescLeft">&#160;</td><td class="mdescRight">Minimum segment size <br /></td></tr>
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<tr class="memitem:a40779a978d7208a82cc9421c4033a1e4 inherit pro_attribs_classpcl_1_1_l_c_c_p_segmentation"><td class="memItemLeft" align="right" valign="top">std::map&lt; uint32_t, bool &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_l_c_c_p_segmentation.html#a40779a978d7208a82cc9421c4033a1e4">processed_</a></td></tr>
<tr class="memdesc:a40779a978d7208a82cc9421c4033a1e4 inherit pro_attribs_classpcl_1_1_l_c_c_p_segmentation"><td class="mdescLeft">&#160;</td><td class="mdescRight">Stores which supervoxel labels were already visited during recursive grouping. <br  />
  <a href="classpcl_1_1_l_c_c_p_segmentation.html#a40779a978d7208a82cc9421c4033a1e4">更多...</a><br /></td></tr>
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<tr class="memitem:adff367bb7eab2ec652da194f36ad2ab4 inherit pro_attribs_classpcl_1_1_l_c_c_p_segmentation"><td class="memItemLeft" align="right" valign="top"><a id="adff367bb7eab2ec652da194f36ad2ab4"></a>
SupervoxelAdjacencyList&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_l_c_c_p_segmentation.html#adff367bb7eab2ec652da194f36ad2ab4">sv_adjacency_list_</a></td></tr>
<tr class="memdesc:adff367bb7eab2ec652da194f36ad2ab4 inherit pro_attribs_classpcl_1_1_l_c_c_p_segmentation"><td class="mdescLeft">&#160;</td><td class="mdescRight">Adjacency graph with the supervoxel labels as nodes and edges between adjacent supervoxels <br /></td></tr>
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std::map&lt; uint32_t, typename <a class="el" href="classpcl_1_1_supervoxel.html">pcl::Supervoxel</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;::Ptr &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_l_c_c_p_segmentation.html#a6e8c0fd169543d42903904b02d36239b">sv_label_to_supervoxel_map_</a></td></tr>
<tr class="memdesc:a6e8c0fd169543d42903904b02d36239b inherit pro_attribs_classpcl_1_1_l_c_c_p_segmentation"><td class="mdescLeft">&#160;</td><td class="mdescRight">map from the supervoxel labels to the supervoxel objects <br  />
 <br /></td></tr>
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<tr class="memitem:afb6ff37d270e3f16c69c46560a1fafce inherit pro_attribs_classpcl_1_1_l_c_c_p_segmentation"><td class="memItemLeft" align="right" valign="top">std::map&lt; uint32_t, uint32_t &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_l_c_c_p_segmentation.html#afb6ff37d270e3f16c69c46560a1fafce">sv_label_to_seg_label_map_</a></td></tr>
<tr class="memdesc:afb6ff37d270e3f16c69c46560a1fafce inherit pro_attribs_classpcl_1_1_l_c_c_p_segmentation"><td class="mdescLeft">&#160;</td><td class="mdescRight">Storing relation between original SuperVoxel Labels and new segmantion labels.  <a href="classpcl_1_1_l_c_c_p_segmentation.html#afb6ff37d270e3f16c69c46560a1fafce">更多...</a><br /></td></tr>
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<tr class="memitem:af38c9be4e674843ba21de27b73ca0189 inherit pro_attribs_classpcl_1_1_l_c_c_p_segmentation"><td class="memItemLeft" align="right" valign="top"><a id="af38c9be4e674843ba21de27b73ca0189"></a>
std::map&lt; uint32_t, std::set&lt; uint32_t &gt; &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_l_c_c_p_segmentation.html#af38c9be4e674843ba21de27b73ca0189">seg_label_to_sv_list_map_</a></td></tr>
<tr class="memdesc:af38c9be4e674843ba21de27b73ca0189 inherit pro_attribs_classpcl_1_1_l_c_c_p_segmentation"><td class="mdescLeft">&#160;</td><td class="mdescRight">map &lt;Segment <a class="el" href="structpcl_1_1_label.html">Label</a>, std::set &lt;SuperVoxel Labels&gt; &gt; <br /></td></tr>
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<tr class="memitem:a7f426262406a9f9cec6248cdfa5a205c inherit pro_attribs_classpcl_1_1_l_c_c_p_segmentation"><td class="memItemLeft" align="right" valign="top"><a id="a7f426262406a9f9cec6248cdfa5a205c"></a>
std::map&lt; uint32_t, std::set&lt; uint32_t &gt; &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_l_c_c_p_segmentation.html#a7f426262406a9f9cec6248cdfa5a205c">seg_label_to_neighbor_set_map_</a></td></tr>
<tr class="memdesc:a7f426262406a9f9cec6248cdfa5a205c inherit pro_attribs_classpcl_1_1_l_c_c_p_segmentation"><td class="mdescLeft">&#160;</td><td class="mdescRight">map &lt; SegmentID, std::set&lt; Neighboring segment labels&gt; &gt; <br /></td></tr>
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</table>
<a name="details" id="details"></a><h2 class="groupheader">详细描述</h2>
<div class="textblock"><h3>template&lt;typename PointT&gt;<br />
class pcl::CPCSegmentation&lt; PointT &gt;</h3>

<p>A segmentation algorithm partitioning a supervoxel graph. It uses planar cuts induced by local concavities for the recursive segmentation. Cuts are estimated using locally constrained directed RANSAC. </p>
<dl class="section note"><dt>注解</dt><dd>If you use this in a scientific work please cite the following paper: M. Schoeler, J. Papon, F. Woergoetter Constrained Planar Cuts - <a class="el" href="class_object.html">Object</a> Partitioning for Point Clouds In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2015 Inherits most of its functionality from <a class="el" href="classpcl_1_1_l_c_c_p_segmentation.html">LCCPSegmentation</a> </dd></dl>
<dl class="section author"><dt>作者</dt><dd>Markus Schoeler (<a href="#" onclick="location.href='mai'+'lto:'+'msc'+'ho'+'ele'+'r@'+'web'+'.d'+'e'; return false;">mscho<span style="display: none;">.nosp@m.</span>eler<span style="display: none;">.nosp@m.</span>@web.<span style="display: none;">.nosp@m.</span>de</a>) </dd></dl>
</div><h2 class="groupheader">成员函数说明</h2>
<a id="aba7a4f7d9481b0c9c88edc6d301964d9"></a>
<h2 class="memtitle"><span class="permalink"><a href="#aba7a4f7d9481b0c9c88edc6d301964d9">&#9670;&nbsp;</a></span>applyCuttingPlane()</h2>

<div class="memitem">
<div class="memproto">
<div class="memtemplate">
template&lt;typename PointT &gt; </div>
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">void <a class="el" href="classpcl_1_1_c_p_c_segmentation.html">pcl::CPCSegmentation</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;::applyCuttingPlane </td>
          <td>(</td>
          <td class="paramtype">uint32_t&#160;</td>
          <td class="paramname"><em>depth_levels_left</em></td><td>)</td>
          <td></td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">private</span></span>  </td>
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</div><div class="memdoc">

<p>Used in for CPC to find and fit cutting planes to the pointcloud. </p>
<dl class="section note"><dt>注解</dt><dd>Is used recursively </dd></dl>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">depth_levels_left</td><td>When first calling the function set this parameter to the maximum levels you want to cut down </td></tr>
  </table>
  </dd>
</dl>
<div class="fragment"><div class="line"><a name="l00087"></a><span class="lineno">   87</span>&#160;{</div>
<div class="line"><a name="l00088"></a><span class="lineno">   88</span>&#160;  <span class="keyword">typedef</span> std::map&lt;uint32_t, pcl::PointCloud&lt;WeightSACPointType&gt;::Ptr&gt; SegLabel2ClusterMap;</div>
<div class="line"><a name="l00089"></a><span class="lineno">   89</span>&#160;  </div>
<div class="line"><a name="l00090"></a><span class="lineno">   90</span>&#160;  pcl::console::print_info (<span class="stringliteral">&quot;Cutting at level %d (maximum %d)\n&quot;</span>, <a class="code" href="classpcl_1_1_c_p_c_segmentation.html#a6293e15b7d22fbb19d7819bedba683f1">max_cuts_</a> - depth_levels_left + 1, <a class="code" href="classpcl_1_1_c_p_c_segmentation.html#a6293e15b7d22fbb19d7819bedba683f1">max_cuts_</a>);</div>
<div class="line"><a name="l00091"></a><span class="lineno">   91</span>&#160;  <span class="comment">// stop if we reached the 0 level</span></div>
<div class="line"><a name="l00092"></a><span class="lineno">   92</span>&#160;  <span class="keywordflow">if</span> (depth_levels_left &lt;= 0)</div>
<div class="line"><a name="l00093"></a><span class="lineno">   93</span>&#160;    <span class="keywordflow">return</span>;</div>
<div class="line"><a name="l00094"></a><span class="lineno">   94</span>&#160; </div>
<div class="line"><a name="l00095"></a><span class="lineno">   95</span>&#160;  SegLabel2ClusterMap seg_to_edge_points_map;</div>
<div class="line"><a name="l00096"></a><span class="lineno">   96</span>&#160;  std::map&lt;uint32_t, std::vector&lt;EdgeID&gt; &gt; seg_to_edgeIDs_map;</div>
<div class="line"><a name="l00097"></a><span class="lineno">   97</span>&#160;  EdgeIterator edge_itr, edge_itr_end, next_edge;</div>
<div class="line"><a name="l00098"></a><span class="lineno">   98</span>&#160;  boost::tie (edge_itr, edge_itr_end) = boost::edges (<a class="code" href="classpcl_1_1_c_p_c_segmentation.html#adff367bb7eab2ec652da194f36ad2ab4">sv_adjacency_list_</a>);</div>
<div class="line"><a name="l00099"></a><span class="lineno">   99</span>&#160;  <span class="keywordflow">for</span> (next_edge = edge_itr; edge_itr != edge_itr_end; edge_itr = next_edge)</div>
<div class="line"><a name="l00100"></a><span class="lineno">  100</span>&#160;  {</div>
<div class="line"><a name="l00101"></a><span class="lineno">  101</span>&#160;    next_edge++;  <span class="comment">// next_edge iterator is neccessary, because removing an edge invalidates the iterator to the current edge</span></div>
<div class="line"><a name="l00102"></a><span class="lineno">  102</span>&#160;    uint32_t source_sv_label = <a class="code" href="classpcl_1_1_c_p_c_segmentation.html#adff367bb7eab2ec652da194f36ad2ab4">sv_adjacency_list_</a>[boost::source (*edge_itr, <a class="code" href="classpcl_1_1_c_p_c_segmentation.html#adff367bb7eab2ec652da194f36ad2ab4">sv_adjacency_list_</a>)];</div>
<div class="line"><a name="l00103"></a><span class="lineno">  103</span>&#160;    uint32_t target_sv_label = <a class="code" href="classpcl_1_1_c_p_c_segmentation.html#adff367bb7eab2ec652da194f36ad2ab4">sv_adjacency_list_</a>[boost::target (*edge_itr, <a class="code" href="classpcl_1_1_c_p_c_segmentation.html#adff367bb7eab2ec652da194f36ad2ab4">sv_adjacency_list_</a>)];</div>
<div class="line"><a name="l00104"></a><span class="lineno">  104</span>&#160; </div>
<div class="line"><a name="l00105"></a><span class="lineno">  105</span>&#160;    uint32_t source_segment_label = <a class="code" href="classpcl_1_1_c_p_c_segmentation.html#afb6ff37d270e3f16c69c46560a1fafce">sv_label_to_seg_label_map_</a>[source_sv_label];</div>
<div class="line"><a name="l00106"></a><span class="lineno">  106</span>&#160;    uint32_t target_segment_label = <a class="code" href="classpcl_1_1_c_p_c_segmentation.html#afb6ff37d270e3f16c69c46560a1fafce">sv_label_to_seg_label_map_</a>[target_sv_label];</div>
<div class="line"><a name="l00107"></a><span class="lineno">  107</span>&#160; </div>
<div class="line"><a name="l00108"></a><span class="lineno">  108</span>&#160;    <span class="comment">// do not process edges which already split two segments</span></div>
<div class="line"><a name="l00109"></a><span class="lineno">  109</span>&#160;    <span class="keywordflow">if</span> (source_segment_label != target_segment_label)</div>
<div class="line"><a name="l00110"></a><span class="lineno">  110</span>&#160;      <span class="keywordflow">continue</span>;</div>
<div class="line"><a name="l00111"></a><span class="lineno">  111</span>&#160; </div>
<div class="line"><a name="l00112"></a><span class="lineno">  112</span>&#160;    <span class="comment">// if edge has been used for cutting already do not use it again</span></div>
<div class="line"><a name="l00113"></a><span class="lineno">  113</span>&#160;    <span class="keywordflow">if</span> (<a class="code" href="classpcl_1_1_c_p_c_segmentation.html#adff367bb7eab2ec652da194f36ad2ab4">sv_adjacency_list_</a>[*edge_itr].used_for_cutting)</div>
<div class="line"><a name="l00114"></a><span class="lineno">  114</span>&#160;      <span class="keywordflow">continue</span>;</div>
<div class="line"><a name="l00115"></a><span class="lineno">  115</span>&#160;    <span class="comment">// get centroids of vertices</span></div>
<div class="line"><a name="l00116"></a><span class="lineno">  116</span>&#160;    <span class="keyword">const</span> <a class="code" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">pcl::PointXYZRGBA</a> source_centroid = <a class="code" href="classpcl_1_1_c_p_c_segmentation.html#a6e8c0fd169543d42903904b02d36239b">sv_label_to_supervoxel_map_</a>[source_sv_label]-&gt;centroid_;</div>
<div class="line"><a name="l00117"></a><span class="lineno">  117</span>&#160;    <span class="keyword">const</span> <a class="code" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">pcl::PointXYZRGBA</a> target_centroid = <a class="code" href="classpcl_1_1_c_p_c_segmentation.html#a6e8c0fd169543d42903904b02d36239b">sv_label_to_supervoxel_map_</a>[target_sv_label]-&gt;centroid_;</div>
<div class="line"><a name="l00118"></a><span class="lineno">  118</span>&#160; </div>
<div class="line"><a name="l00119"></a><span class="lineno">  119</span>&#160;    <span class="comment">// stores the information about the edge cloud (used for the weighted ransac)</span></div>
<div class="line"><a name="l00120"></a><span class="lineno">  120</span>&#160;    <span class="comment">// we use the normal to express the direction of the connection</span></div>
<div class="line"><a name="l00121"></a><span class="lineno">  121</span>&#160;    <span class="comment">// we use the intensity to express the normal differences between supervoxel patches. &lt;=0: Convex, &gt;0: Concave</span></div>
<div class="line"><a name="l00122"></a><span class="lineno">  122</span>&#160;    WeightSACPointType edge_centroid;</div>
<div class="line"><a name="l00123"></a><span class="lineno">  123</span>&#160;    edge_centroid.getVector3fMap () = (source_centroid.getVector3fMap () + target_centroid.getVector3fMap ()) / 2;</div>
<div class="line"><a name="l00124"></a><span class="lineno">  124</span>&#160; </div>
<div class="line"><a name="l00125"></a><span class="lineno">  125</span>&#160;    <span class="comment">// we use the normal to express the direction of the connection!</span></div>
<div class="line"><a name="l00126"></a><span class="lineno">  126</span>&#160;    edge_centroid.getNormalVector3fMap () = (target_centroid.getVector3fMap () - source_centroid.getVector3fMap ()).normalized ();</div>
<div class="line"><a name="l00127"></a><span class="lineno">  127</span>&#160; </div>
<div class="line"><a name="l00128"></a><span class="lineno">  128</span>&#160;    <span class="comment">// we use the intensity to express the normal differences between supervoxel patches. &lt;=0: Convex, &gt;0: Concave</span></div>
<div class="line"><a name="l00129"></a><span class="lineno">  129</span>&#160;    edge_centroid.intensity = <a class="code" href="classpcl_1_1_c_p_c_segmentation.html#adff367bb7eab2ec652da194f36ad2ab4">sv_adjacency_list_</a>[*edge_itr].is_convex ? -<a class="code" href="classpcl_1_1_c_p_c_segmentation.html#adff367bb7eab2ec652da194f36ad2ab4">sv_adjacency_list_</a>[*edge_itr].normal_difference : <a class="code" href="classpcl_1_1_c_p_c_segmentation.html#adff367bb7eab2ec652da194f36ad2ab4">sv_adjacency_list_</a>[*edge_itr].normal_difference;</div>
<div class="line"><a name="l00130"></a><span class="lineno">  130</span>&#160;    <span class="keywordflow">if</span> (seg_to_edge_points_map.find (source_segment_label) == seg_to_edge_points_map.end ())</div>
<div class="line"><a name="l00131"></a><span class="lineno">  131</span>&#160;    {</div>
<div class="line"><a name="l00132"></a><span class="lineno">  132</span>&#160;      seg_to_edge_points_map[source_segment_label] = pcl::PointCloud&lt;WeightSACPointType&gt;::Ptr (<span class="keyword">new</span> <a class="code" href="classpcl_1_1_point_cloud.html">pcl::PointCloud&lt;WeightSACPointType&gt;</a> ());</div>
<div class="line"><a name="l00133"></a><span class="lineno">  133</span>&#160;    }</div>
<div class="line"><a name="l00134"></a><span class="lineno">  134</span>&#160;    seg_to_edge_points_map[source_segment_label]-&gt;push_back (edge_centroid);</div>
<div class="line"><a name="l00135"></a><span class="lineno">  135</span>&#160;    seg_to_edgeIDs_map[source_segment_label].push_back (*edge_itr);</div>
<div class="line"><a name="l00136"></a><span class="lineno">  136</span>&#160;  }</div>
<div class="line"><a name="l00137"></a><span class="lineno">  137</span>&#160;  <span class="keywordtype">bool</span> cut_found = <span class="keyword">false</span>;</div>
<div class="line"><a name="l00138"></a><span class="lineno">  138</span>&#160;  <span class="comment">// do the following processing for each segment separately</span></div>
<div class="line"><a name="l00139"></a><span class="lineno">  139</span>&#160;  <span class="keywordflow">for</span> (SegLabel2ClusterMap::iterator itr = seg_to_edge_points_map.begin (); itr != seg_to_edge_points_map.end (); ++itr)</div>
<div class="line"><a name="l00140"></a><span class="lineno">  140</span>&#160;  {</div>
<div class="line"><a name="l00141"></a><span class="lineno">  141</span>&#160;    <span class="comment">// if too small do not process</span></div>
<div class="line"><a name="l00142"></a><span class="lineno">  142</span>&#160;    <span class="keywordflow">if</span> (itr-&gt;second-&gt;size () &lt; <a class="code" href="classpcl_1_1_c_p_c_segmentation.html#a6eda4c6ab0c6d0f55b11c5a666accd7f">min_segment_size_for_cutting_</a>)</div>
<div class="line"><a name="l00143"></a><span class="lineno">  143</span>&#160;    {</div>
<div class="line"><a name="l00144"></a><span class="lineno">  144</span>&#160;      <span class="keywordflow">continue</span>;</div>
<div class="line"><a name="l00145"></a><span class="lineno">  145</span>&#160;    }</div>
<div class="line"><a name="l00146"></a><span class="lineno">  146</span>&#160; </div>
<div class="line"><a name="l00147"></a><span class="lineno">  147</span>&#160;    std::vector&lt;double&gt; weights;</div>
<div class="line"><a name="l00148"></a><span class="lineno">  148</span>&#160;    weights.resize (itr-&gt;second-&gt;size ());</div>
<div class="line"><a name="l00149"></a><span class="lineno">  149</span>&#160;    <span class="keywordflow">for</span> (std::size_t cp = 0; cp &lt; itr-&gt;second-&gt;size (); ++cp)</div>
<div class="line"><a name="l00150"></a><span class="lineno">  150</span>&#160;    {</div>
<div class="line"><a name="l00151"></a><span class="lineno">  151</span>&#160;      <span class="keywordtype">float</span>&amp; cur_weight = itr-&gt;second-&gt;points[cp].intensity;</div>
<div class="line"><a name="l00152"></a><span class="lineno">  152</span>&#160;      cur_weight = cur_weight &lt; <a class="code" href="classpcl_1_1_c_p_c_segmentation.html#a90f2ad90bee047f31f2c9ad4f3b0c158">concavity_tolerance_threshold_</a> ? 0 : 1;</div>
<div class="line"><a name="l00153"></a><span class="lineno">  153</span>&#160;      weights[cp] = cur_weight;</div>
<div class="line"><a name="l00154"></a><span class="lineno">  154</span>&#160;    }</div>
<div class="line"><a name="l00155"></a><span class="lineno">  155</span>&#160; </div>
<div class="line"><a name="l00156"></a><span class="lineno">  156</span>&#160;    pcl::PointCloud&lt;WeightSACPointType&gt;::Ptr edge_cloud_cluster  = itr-&gt;second;</div>
<div class="line"><a name="l00157"></a><span class="lineno">  157</span>&#160;    pcl::SampleConsensusModelPlane&lt;WeightSACPointType&gt;::Ptr model_p (<span class="keyword">new</span> <a class="code" href="classpcl_1_1_sample_consensus_model_plane.html">pcl::SampleConsensusModelPlane&lt;WeightSACPointType&gt;</a> (edge_cloud_cluster));</div>
<div class="line"><a name="l00158"></a><span class="lineno">  158</span>&#160; </div>
<div class="line"><a name="l00159"></a><span class="lineno">  159</span>&#160;    WeightedRandomSampleConsensus weight_sac (model_p, <a class="code" href="classpcl_1_1_c_p_c_segmentation.html#aa9f7011e99af9d3849937ff5370c2e11">seed_resolution_</a>, <span class="keyword">true</span>);</div>
<div class="line"><a name="l00160"></a><span class="lineno">  160</span>&#160; </div>
<div class="line"><a name="l00161"></a><span class="lineno">  161</span>&#160;    weight_sac.setWeights (weights, <a class="code" href="classpcl_1_1_c_p_c_segmentation.html#aceaa9f8130856b9304830674ac7515c8">use_directed_weights_</a>);</div>
<div class="line"><a name="l00162"></a><span class="lineno">  162</span>&#160;    weight_sac.setMaxIterations (<a class="code" href="classpcl_1_1_c_p_c_segmentation.html#aa1eac80686a308fdd04592bdefd9e6bd">ransac_itrs_</a>);</div>
<div class="line"><a name="l00163"></a><span class="lineno">  163</span>&#160; </div>
<div class="line"><a name="l00164"></a><span class="lineno">  164</span>&#160;    <span class="comment">// if not enough inliers are found</span></div>
<div class="line"><a name="l00165"></a><span class="lineno">  165</span>&#160;    <span class="keywordflow">if</span> (!weight_sac.computeModel ())</div>
<div class="line"><a name="l00166"></a><span class="lineno">  166</span>&#160;    {</div>
<div class="line"><a name="l00167"></a><span class="lineno">  167</span>&#160;      <span class="keywordflow">continue</span>;</div>
<div class="line"><a name="l00168"></a><span class="lineno">  168</span>&#160;    }</div>
<div class="line"><a name="l00169"></a><span class="lineno">  169</span>&#160; </div>
<div class="line"><a name="l00170"></a><span class="lineno">  170</span>&#160;    Eigen::VectorXf model_coefficients;</div>
<div class="line"><a name="l00171"></a><span class="lineno">  171</span>&#160;    weight_sac.getModelCoefficients (model_coefficients);</div>
<div class="line"><a name="l00172"></a><span class="lineno">  172</span>&#160; </div>
<div class="line"><a name="l00173"></a><span class="lineno">  173</span>&#160;    model_coefficients[3] += std::numeric_limits&lt;float&gt;::epsilon ();    </div>
<div class="line"><a name="l00174"></a><span class="lineno">  174</span>&#160; </div>
<div class="line"><a name="l00175"></a><span class="lineno">  175</span>&#160;    std::vector&lt;int&gt; support_indices;</div>
<div class="line"><a name="l00176"></a><span class="lineno">  176</span>&#160;    weight_sac.getInliers (support_indices);</div>
<div class="line"><a name="l00177"></a><span class="lineno">  177</span>&#160; </div>
<div class="line"><a name="l00178"></a><span class="lineno">  178</span>&#160;    <span class="comment">// the support_indices which are actually cut (if not locally constrain:  cut_support_indices = support_indices</span></div>
<div class="line"><a name="l00179"></a><span class="lineno">  179</span>&#160;    std::vector&lt;int&gt; cut_support_indices;</div>
<div class="line"><a name="l00180"></a><span class="lineno">  180</span>&#160; </div>
<div class="line"><a name="l00181"></a><span class="lineno">  181</span>&#160;    <span class="keywordflow">if</span> (<a class="code" href="classpcl_1_1_c_p_c_segmentation.html#a6016df56b09c1f9b13db954e1805d930">use_local_constrains_</a>)</div>
<div class="line"><a name="l00182"></a><span class="lineno">  182</span>&#160;    {</div>
<div class="line"><a name="l00183"></a><span class="lineno">  183</span>&#160;      Eigen::Vector3f plane_normal (model_coefficients[0], model_coefficients[1], model_coefficients[2]);</div>
<div class="line"><a name="l00184"></a><span class="lineno">  184</span>&#160;      <span class="comment">// Cut the connections.</span></div>
<div class="line"><a name="l00185"></a><span class="lineno">  185</span>&#160;      <span class="comment">// We only interate through the points which are within the support (when we are local, otherwise all points in the segment).</span></div>
<div class="line"><a name="l00186"></a><span class="lineno">  186</span>&#160;      <span class="comment">// We also just acutally cut when the edge goes through the plane. This is why we check the planedistance</span></div>
<div class="line"><a name="l00187"></a><span class="lineno">  187</span>&#160;      std::vector&lt;pcl::PointIndices&gt; cluster_indices;</div>
<div class="line"><a name="l00188"></a><span class="lineno">  188</span>&#160;      <a class="code" href="classpcl_1_1_euclidean_cluster_extraction.html">pcl::EuclideanClusterExtraction&lt;WeightSACPointType&gt;</a> euclidean_clusterer;</div>
<div class="line"><a name="l00189"></a><span class="lineno">  189</span>&#160;      pcl::search::KdTree&lt;WeightSACPointType&gt;::Ptr tree (<span class="keyword">new</span> <a class="code" href="classpcl_1_1search_1_1_kd_tree.html">pcl::search::KdTree&lt;WeightSACPointType&gt;</a>);</div>
<div class="line"><a name="l00190"></a><span class="lineno">  190</span>&#160;      tree-&gt;setInputCloud (edge_cloud_cluster);</div>
<div class="line"><a name="l00191"></a><span class="lineno">  191</span>&#160;      euclidean_clusterer.<a class="code" href="classpcl_1_1_euclidean_cluster_extraction.html#a8fb42fea2e8bfca4ebadf4339335cf11">setClusterTolerance</a> (<a class="code" href="classpcl_1_1_c_p_c_segmentation.html#aa9f7011e99af9d3849937ff5370c2e11">seed_resolution_</a>);</div>
<div class="line"><a name="l00192"></a><span class="lineno">  192</span>&#160;      euclidean_clusterer.<a class="code" href="classpcl_1_1_euclidean_cluster_extraction.html#a096af3508dd19b23a726a8323f7c7bba">setMinClusterSize</a> (1);</div>
<div class="line"><a name="l00193"></a><span class="lineno">  193</span>&#160;      euclidean_clusterer.<a class="code" href="classpcl_1_1_euclidean_cluster_extraction.html#adb0be906f101b309506cdc37ffd31624">setMaxClusterSize</a> (25000);</div>
<div class="line"><a name="l00194"></a><span class="lineno">  194</span>&#160;      euclidean_clusterer.<a class="code" href="classpcl_1_1_euclidean_cluster_extraction.html#ac4162a11c1fd5797d507068a056bfbf7">setSearchMethod</a> (tree);</div>
<div class="line"><a name="l00195"></a><span class="lineno">  195</span>&#160;      euclidean_clusterer.<a class="code" href="classpcl_1_1_p_c_l_base.html#a1952d7101f3942bac3b69ed55c1ca7ea">setInputCloud</a> (edge_cloud_cluster);</div>
<div class="line"><a name="l00196"></a><span class="lineno">  196</span>&#160;      euclidean_clusterer.<a class="code" href="classpcl_1_1_p_c_l_base.html#ab219359de6eb34c9d51e2e976dd1a0d1">setIndices</a> (boost::make_shared &lt;std::vector &lt;int&gt; &gt; (support_indices));</div>
<div class="line"><a name="l00197"></a><span class="lineno">  197</span>&#160;      euclidean_clusterer.<a class="code" href="classpcl_1_1_euclidean_cluster_extraction.html#a41e0cd5e3f7967d59013c967c909585c">extract</a> (cluster_indices);</div>
<div class="line"><a name="l00198"></a><span class="lineno">  198</span>&#160;<span class="comment">//       sv_adjacency_list_[seg_to_edgeID_map[itr-&gt;first][point_index]].used_for_cutting = true;</span></div>
<div class="line"><a name="l00199"></a><span class="lineno">  199</span>&#160; </div>
<div class="line"><a name="l00200"></a><span class="lineno">  200</span>&#160;      <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> cc = 0; cc &lt; cluster_indices.size (); ++cc)</div>
<div class="line"><a name="l00201"></a><span class="lineno">  201</span>&#160;      {</div>
<div class="line"><a name="l00202"></a><span class="lineno">  202</span>&#160;        <span class="comment">// get centroids of vertices        </span></div>
<div class="line"><a name="l00203"></a><span class="lineno">  203</span>&#160;        <span class="keywordtype">int</span> cluster_concave_pts = 0;</div>
<div class="line"><a name="l00204"></a><span class="lineno">  204</span>&#160;        <span class="keywordtype">float</span> cluster_score = 0;</div>
<div class="line"><a name="l00205"></a><span class="lineno">  205</span>&#160;<span class="comment">//         std::cout &lt;&lt; &quot;Cluster has &quot; &lt;&lt; cluster_indices[cc].indices.size () &lt;&lt; &quot; points&quot; &lt;&lt; std::endl;</span></div>
<div class="line"><a name="l00206"></a><span class="lineno">  206</span>&#160;        <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> cp = 0; cp &lt; cluster_indices[cc].indices.size (); ++cp)</div>
<div class="line"><a name="l00207"></a><span class="lineno">  207</span>&#160;        {</div>
<div class="line"><a name="l00208"></a><span class="lineno">  208</span>&#160;          <span class="keywordtype">int</span> current_index = cluster_indices[cc].indices[cp];</div>
<div class="line"><a name="l00209"></a><span class="lineno">  209</span>&#160;          <span class="keywordtype">double</span> index_score;</div>
<div class="line"><a name="l00210"></a><span class="lineno">  210</span>&#160;          <span class="keywordflow">if</span> (<a class="code" href="classpcl_1_1_c_p_c_segmentation.html#aceaa9f8130856b9304830674ac7515c8">use_directed_weights_</a>)</div>
<div class="line"><a name="l00211"></a><span class="lineno">  211</span>&#160;            index_score = weights[current_index] * 1.414 * (fabsf (plane_normal.dot (edge_cloud_cluster-&gt;<a class="code" href="classpcl_1_1_point_cloud.html#a1155fe4ba5cdc7e83cb72159a4ea02dc">at</a> (current_index).getNormalVector3fMap ())));</div>
<div class="line"><a name="l00212"></a><span class="lineno">  212</span>&#160;          <span class="keywordflow">else</span></div>
<div class="line"><a name="l00213"></a><span class="lineno">  213</span>&#160;            index_score = weights[current_index];</div>
<div class="line"><a name="l00214"></a><span class="lineno">  214</span>&#160;          cluster_score += index_score;</div>
<div class="line"><a name="l00215"></a><span class="lineno">  215</span>&#160;          <span class="keywordflow">if</span> (weights[current_index] &gt; 0)</div>
<div class="line"><a name="l00216"></a><span class="lineno">  216</span>&#160;            ++cluster_concave_pts;</div>
<div class="line"><a name="l00217"></a><span class="lineno">  217</span>&#160;        }</div>
<div class="line"><a name="l00218"></a><span class="lineno">  218</span>&#160;        <span class="comment">// check if the score is below the threshold. If that is the case this segment should not be split</span></div>
<div class="line"><a name="l00219"></a><span class="lineno">  219</span>&#160;        cluster_score = cluster_score * 1.0 / cluster_indices[cc].indices.size ();</div>
<div class="line"><a name="l00220"></a><span class="lineno">  220</span>&#160;<span class="comment">//         std::cout &lt;&lt; &quot;Cluster score: &quot; &lt;&lt; cluster_score &lt;&lt; std::endl;</span></div>
<div class="line"><a name="l00221"></a><span class="lineno">  221</span>&#160;        <span class="keywordflow">if</span> (cluster_score &gt;= <a class="code" href="classpcl_1_1_c_p_c_segmentation.html#ac04198491da197fdbb9478dbf682f1ec">min_cut_score_</a>)</div>
<div class="line"><a name="l00222"></a><span class="lineno">  222</span>&#160;        {</div>
<div class="line"><a name="l00223"></a><span class="lineno">  223</span>&#160;          cut_support_indices.insert (cut_support_indices.end (), cluster_indices[cc].indices.begin (), cluster_indices[cc].indices.end ());</div>
<div class="line"><a name="l00224"></a><span class="lineno">  224</span>&#160;        }</div>
<div class="line"><a name="l00225"></a><span class="lineno">  225</span>&#160;      }</div>
<div class="line"><a name="l00226"></a><span class="lineno">  226</span>&#160;      <span class="keywordflow">if</span> (cut_support_indices.size () == 0)</div>
<div class="line"><a name="l00227"></a><span class="lineno">  227</span>&#160;      {</div>
<div class="line"><a name="l00228"></a><span class="lineno">  228</span>&#160;<span class="comment">//         std::cout &lt;&lt; &quot;Could not find planes which exceed required minumum score (threshold &quot; &lt;&lt; min_cut_score_ &lt;&lt; &quot;), not cutting&quot; &lt;&lt; std::endl;</span></div>
<div class="line"><a name="l00229"></a><span class="lineno">  229</span>&#160;        <span class="keywordflow">continue</span>;</div>
<div class="line"><a name="l00230"></a><span class="lineno">  230</span>&#160;      }</div>
<div class="line"><a name="l00231"></a><span class="lineno">  231</span>&#160;    }</div>
<div class="line"><a name="l00232"></a><span class="lineno">  232</span>&#160;    <span class="keywordflow">else</span></div>
<div class="line"><a name="l00233"></a><span class="lineno">  233</span>&#160;    {</div>
<div class="line"><a name="l00234"></a><span class="lineno">  234</span>&#160;      <span class="keywordtype">double</span> current_score = weight_sac.getBestScore ();</div>
<div class="line"><a name="l00235"></a><span class="lineno">  235</span>&#160;      cut_support_indices = support_indices;</div>
<div class="line"><a name="l00236"></a><span class="lineno">  236</span>&#160;      <span class="comment">// check if the score is below the threshold. If that is the case this segment should not be split</span></div>
<div class="line"><a name="l00237"></a><span class="lineno">  237</span>&#160;      <span class="keywordflow">if</span> (current_score &lt; <a class="code" href="classpcl_1_1_c_p_c_segmentation.html#ac04198491da197fdbb9478dbf682f1ec">min_cut_score_</a>)</div>
<div class="line"><a name="l00238"></a><span class="lineno">  238</span>&#160;      {</div>
<div class="line"><a name="l00239"></a><span class="lineno">  239</span>&#160;<span class="comment">//         std::cout &lt;&lt; &quot;Score too low, no cutting&quot; &lt;&lt; std::endl;</span></div>
<div class="line"><a name="l00240"></a><span class="lineno">  240</span>&#160;        <span class="keywordflow">continue</span>;</div>
<div class="line"><a name="l00241"></a><span class="lineno">  241</span>&#160;      }</div>
<div class="line"><a name="l00242"></a><span class="lineno">  242</span>&#160;    }</div>
<div class="line"><a name="l00243"></a><span class="lineno">  243</span>&#160; </div>
<div class="line"><a name="l00244"></a><span class="lineno">  244</span>&#160;    <span class="keywordtype">int</span> number_connections_cut = 0;</div>
<div class="line"><a name="l00245"></a><span class="lineno">  245</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> cs = 0; cs &lt; cut_support_indices.size (); ++cs)</div>
<div class="line"><a name="l00246"></a><span class="lineno">  246</span>&#160;    {</div>
<div class="line"><a name="l00247"></a><span class="lineno">  247</span>&#160;      <span class="keyword">const</span> <span class="keywordtype">int</span> point_index = cut_support_indices[cs];</div>
<div class="line"><a name="l00248"></a><span class="lineno">  248</span>&#160; </div>
<div class="line"><a name="l00249"></a><span class="lineno">  249</span>&#160;      <span class="keywordflow">if</span> (<a class="code" href="classpcl_1_1_c_p_c_segmentation.html#a743b8b7bfcb33d9edfcbd9fa1ecc2eac">use_clean_cutting_</a>)</div>
<div class="line"><a name="l00250"></a><span class="lineno">  250</span>&#160;      {</div>
<div class="line"><a name="l00251"></a><span class="lineno">  251</span>&#160;        <span class="comment">// skip edges where both centroids are on one side of the cutting plane</span></div>
<div class="line"><a name="l00252"></a><span class="lineno">  252</span>&#160;        uint32_t source_sv_label = <a class="code" href="classpcl_1_1_c_p_c_segmentation.html#adff367bb7eab2ec652da194f36ad2ab4">sv_adjacency_list_</a>[boost::source (seg_to_edgeIDs_map[itr-&gt;first][point_index], <a class="code" href="classpcl_1_1_c_p_c_segmentation.html#adff367bb7eab2ec652da194f36ad2ab4">sv_adjacency_list_</a>)];</div>
<div class="line"><a name="l00253"></a><span class="lineno">  253</span>&#160;        uint32_t target_sv_label = <a class="code" href="classpcl_1_1_c_p_c_segmentation.html#adff367bb7eab2ec652da194f36ad2ab4">sv_adjacency_list_</a>[boost::target (seg_to_edgeIDs_map[itr-&gt;first][point_index], <a class="code" href="classpcl_1_1_c_p_c_segmentation.html#adff367bb7eab2ec652da194f36ad2ab4">sv_adjacency_list_</a>)];</div>
<div class="line"><a name="l00254"></a><span class="lineno">  254</span>&#160;        <span class="comment">// get centroids of vertices</span></div>
<div class="line"><a name="l00255"></a><span class="lineno">  255</span>&#160;        <span class="keyword">const</span> <a class="code" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">pcl::PointXYZRGBA</a> source_centroid = <a class="code" href="classpcl_1_1_c_p_c_segmentation.html#a6e8c0fd169543d42903904b02d36239b">sv_label_to_supervoxel_map_</a>[source_sv_label]-&gt;centroid_;</div>
<div class="line"><a name="l00256"></a><span class="lineno">  256</span>&#160;        <span class="keyword">const</span> <a class="code" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">pcl::PointXYZRGBA</a> target_centroid = <a class="code" href="classpcl_1_1_c_p_c_segmentation.html#a6e8c0fd169543d42903904b02d36239b">sv_label_to_supervoxel_map_</a>[target_sv_label]-&gt;centroid_;</div>
<div class="line"><a name="l00257"></a><span class="lineno">  257</span>&#160;        <span class="comment">// this makes a clean cut</span></div>
<div class="line"><a name="l00258"></a><span class="lineno">  258</span>&#160;        <span class="keywordflow">if</span> (pcl::pointToPlaneDistanceSigned (source_centroid, model_coefficients) * pcl::pointToPlaneDistanceSigned (target_centroid, model_coefficients) &gt; 0)</div>
<div class="line"><a name="l00259"></a><span class="lineno">  259</span>&#160;        {</div>
<div class="line"><a name="l00260"></a><span class="lineno">  260</span>&#160;          <span class="keywordflow">continue</span>;</div>
<div class="line"><a name="l00261"></a><span class="lineno">  261</span>&#160;        }</div>
<div class="line"><a name="l00262"></a><span class="lineno">  262</span>&#160;      }</div>
<div class="line"><a name="l00263"></a><span class="lineno">  263</span>&#160;      <a class="code" href="classpcl_1_1_c_p_c_segmentation.html#adff367bb7eab2ec652da194f36ad2ab4">sv_adjacency_list_</a>[seg_to_edgeIDs_map[itr-&gt;first][point_index]].used_for_cutting = <span class="keyword">true</span>;</div>
<div class="line"><a name="l00264"></a><span class="lineno">  264</span>&#160;      <span class="keywordflow">if</span> (<a class="code" href="classpcl_1_1_c_p_c_segmentation.html#adff367bb7eab2ec652da194f36ad2ab4">sv_adjacency_list_</a>[seg_to_edgeIDs_map[itr-&gt;first][point_index]].is_valid) </div>
<div class="line"><a name="l00265"></a><span class="lineno">  265</span>&#160;      {</div>
<div class="line"><a name="l00266"></a><span class="lineno">  266</span>&#160;        ++number_connections_cut;</div>
<div class="line"><a name="l00267"></a><span class="lineno">  267</span>&#160;        <a class="code" href="classpcl_1_1_c_p_c_segmentation.html#adff367bb7eab2ec652da194f36ad2ab4">sv_adjacency_list_</a>[seg_to_edgeIDs_map[itr-&gt;first][point_index]].is_valid = <span class="keyword">false</span>;</div>
<div class="line"><a name="l00268"></a><span class="lineno">  268</span>&#160;      }</div>
<div class="line"><a name="l00269"></a><span class="lineno">  269</span>&#160;    }</div>
<div class="line"><a name="l00270"></a><span class="lineno">  270</span>&#160;<span class="comment">//     std::cout &lt;&lt; &quot;We cut &quot; &lt;&lt; number_connections_cut &lt;&lt; &quot; connections&quot; &lt;&lt; std::endl;</span></div>
<div class="line"><a name="l00271"></a><span class="lineno">  271</span>&#160;    <span class="keywordflow">if</span> (number_connections_cut &gt; 0)</div>
<div class="line"><a name="l00272"></a><span class="lineno">  272</span>&#160;      cut_found = <span class="keyword">true</span>;</div>
<div class="line"><a name="l00273"></a><span class="lineno">  273</span>&#160;  }</div>
<div class="line"><a name="l00274"></a><span class="lineno">  274</span>&#160; </div>
<div class="line"><a name="l00275"></a><span class="lineno">  275</span>&#160;  <span class="comment">// if not cut has been performed we can stop the recursion</span></div>
<div class="line"><a name="l00276"></a><span class="lineno">  276</span>&#160;  <span class="keywordflow">if</span> (cut_found)</div>
<div class="line"><a name="l00277"></a><span class="lineno">  277</span>&#160;  {</div>
<div class="line"><a name="l00278"></a><span class="lineno">  278</span>&#160;    <a class="code" href="classpcl_1_1_c_p_c_segmentation.html#ad1a810ce20594b9a9309c29f089f0d18">doGrouping</a> ();</div>
<div class="line"><a name="l00279"></a><span class="lineno">  279</span>&#160;    --depth_levels_left;</div>
<div class="line"><a name="l00280"></a><span class="lineno">  280</span>&#160;    <a class="code" href="classpcl_1_1_c_p_c_segmentation.html#aba7a4f7d9481b0c9c88edc6d301964d9">applyCuttingPlane</a> (depth_levels_left);</div>
<div class="line"><a name="l00281"></a><span class="lineno">  281</span>&#160;  }</div>
<div class="line"><a name="l00282"></a><span class="lineno">  282</span>&#160;  <span class="keywordflow">else</span></div>
<div class="line"><a name="l00283"></a><span class="lineno">  283</span>&#160;    pcl::console::print_info (<span class="stringliteral">&quot;Could not find any more cuts, stopping recursion\n&quot;</span>);</div>
<div class="line"><a name="l00284"></a><span class="lineno">  284</span>&#160;}</div>
<div class="ttc" id="aclasspcl_1_1_c_p_c_segmentation_html_a6016df56b09c1f9b13db954e1805d930"><div class="ttname"><a href="classpcl_1_1_c_p_c_segmentation.html#a6016df56b09c1f9b13db954e1805d930">pcl::CPCSegmentation::use_local_constrains_</a></div><div class="ttdeci">bool use_local_constrains_</div><div class="ttdoc">Use local constrains for cutting</div><div class="ttdef"><b>Definition:</b> cpc_segmentation.h:152</div></div>
<div class="ttc" id="aclasspcl_1_1_c_p_c_segmentation_html_a6293e15b7d22fbb19d7819bedba683f1"><div class="ttname"><a href="classpcl_1_1_c_p_c_segmentation.html#a6293e15b7d22fbb19d7819bedba683f1">pcl::CPCSegmentation::max_cuts_</a></div><div class="ttdeci">uint32_t max_cuts_</div><div class="ttdoc">*** Parameters *** ///</div><div class="ttdef"><b>Definition:</b> cpc_segmentation.h:143</div></div>
<div class="ttc" id="aclasspcl_1_1_c_p_c_segmentation_html_a6e8c0fd169543d42903904b02d36239b"><div class="ttname"><a href="classpcl_1_1_c_p_c_segmentation.html#a6e8c0fd169543d42903904b02d36239b">pcl::CPCSegmentation::sv_label_to_supervoxel_map_</a></div><div class="ttdeci">std::map&lt; uint32_t, typename pcl::Supervoxel&lt; PointT &gt;::Ptr &gt; sv_label_to_supervoxel_map_</div><div class="ttdoc">map from the supervoxel labels to the supervoxel objects</div><div class="ttdef"><b>Definition:</b> lccp_segmentation.h:339</div></div>
<div class="ttc" id="aclasspcl_1_1_c_p_c_segmentation_html_a6eda4c6ab0c6d0f55b11c5a666accd7f"><div class="ttname"><a href="classpcl_1_1_c_p_c_segmentation.html#a6eda4c6ab0c6d0f55b11c5a666accd7f">pcl::CPCSegmentation::min_segment_size_for_cutting_</a></div><div class="ttdeci">uint32_t min_segment_size_for_cutting_</div><div class="ttdoc">Minimum segment size for cutting</div><div class="ttdef"><b>Definition:</b> cpc_segmentation.h:146</div></div>
<div class="ttc" id="aclasspcl_1_1_c_p_c_segmentation_html_a743b8b7bfcb33d9edfcbd9fa1ecc2eac"><div class="ttname"><a href="classpcl_1_1_c_p_c_segmentation.html#a743b8b7bfcb33d9edfcbd9fa1ecc2eac">pcl::CPCSegmentation::use_clean_cutting_</a></div><div class="ttdeci">bool use_clean_cutting_</div><div class="ttdoc">Use clean cutting</div><div class="ttdef"><b>Definition:</b> cpc_segmentation.h:158</div></div>
<div class="ttc" id="aclasspcl_1_1_c_p_c_segmentation_html_a90f2ad90bee047f31f2c9ad4f3b0c158"><div class="ttname"><a href="classpcl_1_1_c_p_c_segmentation.html#a90f2ad90bee047f31f2c9ad4f3b0c158">pcl::CPCSegmentation::concavity_tolerance_threshold_</a></div><div class="ttdeci">float concavity_tolerance_threshold_</div><div class="ttdoc">*** Parameters *** ///</div><div class="ttdef"><b>Definition:</b> lccp_segmentation.h:302</div></div>
<div class="ttc" id="aclasspcl_1_1_c_p_c_segmentation_html_aa1eac80686a308fdd04592bdefd9e6bd"><div class="ttname"><a href="classpcl_1_1_c_p_c_segmentation.html#aa1eac80686a308fdd04592bdefd9e6bd">pcl::CPCSegmentation::ransac_itrs_</a></div><div class="ttdeci">uint32_t ransac_itrs_</div><div class="ttdoc">Interations for RANSAC</div><div class="ttdef"><b>Definition:</b> cpc_segmentation.h:161</div></div>
<div class="ttc" id="aclasspcl_1_1_c_p_c_segmentation_html_aa9f7011e99af9d3849937ff5370c2e11"><div class="ttname"><a href="classpcl_1_1_c_p_c_segmentation.html#aa9f7011e99af9d3849937ff5370c2e11">pcl::CPCSegmentation::seed_resolution_</a></div><div class="ttdeci">float seed_resolution_</div><div class="ttdoc">Seed resolution of the supervoxels (used only for smoothness check)</div><div class="ttdef"><b>Definition:</b> lccp_segmentation.h:320</div></div>
<div class="ttc" id="aclasspcl_1_1_c_p_c_segmentation_html_aba7a4f7d9481b0c9c88edc6d301964d9"><div class="ttname"><a href="classpcl_1_1_c_p_c_segmentation.html#aba7a4f7d9481b0c9c88edc6d301964d9">pcl::CPCSegmentation::applyCuttingPlane</a></div><div class="ttdeci">void applyCuttingPlane(uint32_t depth_levels_left)</div><div class="ttdoc">Used in for CPC to find and fit cutting planes to the pointcloud.</div><div class="ttdef"><b>Definition:</b> cpc_segmentation.hpp:86</div></div>
<div class="ttc" id="aclasspcl_1_1_c_p_c_segmentation_html_ac04198491da197fdbb9478dbf682f1ec"><div class="ttname"><a href="classpcl_1_1_c_p_c_segmentation.html#ac04198491da197fdbb9478dbf682f1ec">pcl::CPCSegmentation::min_cut_score_</a></div><div class="ttdeci">float min_cut_score_</div><div class="ttdoc">Cut_score threshold</div><div class="ttdef"><b>Definition:</b> cpc_segmentation.h:149</div></div>
<div class="ttc" id="aclasspcl_1_1_c_p_c_segmentation_html_aceaa9f8130856b9304830674ac7515c8"><div class="ttname"><a href="classpcl_1_1_c_p_c_segmentation.html#aceaa9f8130856b9304830674ac7515c8">pcl::CPCSegmentation::use_directed_weights_</a></div><div class="ttdeci">bool use_directed_weights_</div><div class="ttdoc">Use directed weights for the cutting</div><div class="ttdef"><b>Definition:</b> cpc_segmentation.h:155</div></div>
<div class="ttc" id="aclasspcl_1_1_c_p_c_segmentation_html_ad1a810ce20594b9a9309c29f089f0d18"><div class="ttname"><a href="classpcl_1_1_c_p_c_segmentation.html#ad1a810ce20594b9a9309c29f089f0d18">pcl::CPCSegmentation::doGrouping</a></div><div class="ttdeci">void doGrouping()</div><div class="ttdef"><b>Definition:</b> lccp_segmentation.hpp:308</div></div>
<div class="ttc" id="aclasspcl_1_1_c_p_c_segmentation_html_adff367bb7eab2ec652da194f36ad2ab4"><div class="ttname"><a href="classpcl_1_1_c_p_c_segmentation.html#adff367bb7eab2ec652da194f36ad2ab4">pcl::CPCSegmentation::sv_adjacency_list_</a></div><div class="ttdeci">SupervoxelAdjacencyList sv_adjacency_list_</div><div class="ttdoc">Adjacency graph with the supervoxel labels as nodes and edges between adjacent supervoxels</div><div class="ttdef"><b>Definition:</b> lccp_segmentation.h:336</div></div>
<div class="ttc" id="aclasspcl_1_1_c_p_c_segmentation_html_afb6ff37d270e3f16c69c46560a1fafce"><div class="ttname"><a href="classpcl_1_1_c_p_c_segmentation.html#afb6ff37d270e3f16c69c46560a1fafce">pcl::CPCSegmentation::sv_label_to_seg_label_map_</a></div><div class="ttdeci">std::map&lt; uint32_t, uint32_t &gt; sv_label_to_seg_label_map_</div><div class="ttdoc">Storing relation between original SuperVoxel Labels and new segmantion labels.</div><div class="ttdef"><b>Definition:</b> lccp_segmentation.h:343</div></div>
<div class="ttc" id="aclasspcl_1_1_euclidean_cluster_extraction_html"><div class="ttname"><a href="classpcl_1_1_euclidean_cluster_extraction.html">pcl::EuclideanClusterExtraction</a></div><div class="ttdoc">EuclideanClusterExtraction represents a segmentation class for cluster extraction in an Euclidean sen...</div><div class="ttdef"><b>Definition:</b> extract_clusters.h:296</div></div>
<div class="ttc" id="aclasspcl_1_1_euclidean_cluster_extraction_html_a096af3508dd19b23a726a8323f7c7bba"><div class="ttname"><a href="classpcl_1_1_euclidean_cluster_extraction.html#a096af3508dd19b23a726a8323f7c7bba">pcl::EuclideanClusterExtraction::setMinClusterSize</a></div><div class="ttdeci">void setMinClusterSize(int min_cluster_size)</div><div class="ttdoc">Set the minimum number of points that a cluster needs to contain in order to be considered valid.</div><div class="ttdef"><b>Definition:</b> extract_clusters.h:356</div></div>
<div class="ttc" id="aclasspcl_1_1_euclidean_cluster_extraction_html_a41e0cd5e3f7967d59013c967c909585c"><div class="ttname"><a href="classpcl_1_1_euclidean_cluster_extraction.html#a41e0cd5e3f7967d59013c967c909585c">pcl::EuclideanClusterExtraction::extract</a></div><div class="ttdeci">void extract(std::vector&lt; PointIndices &gt; &amp;clusters)</div><div class="ttdoc">Cluster extraction in a PointCloud given by &lt;setInputCloud (), setIndices ()&gt;</div><div class="ttdef"><b>Definition:</b> extract_clusters.hpp:210</div></div>
<div class="ttc" id="aclasspcl_1_1_euclidean_cluster_extraction_html_a8fb42fea2e8bfca4ebadf4339335cf11"><div class="ttname"><a href="classpcl_1_1_euclidean_cluster_extraction.html#a8fb42fea2e8bfca4ebadf4339335cf11">pcl::EuclideanClusterExtraction::setClusterTolerance</a></div><div class="ttdeci">void setClusterTolerance(double tolerance)</div><div class="ttdoc">Set the spatial cluster tolerance as a measure in the L2 Euclidean space</div><div class="ttdef"><b>Definition:</b> extract_clusters.h:340</div></div>
<div class="ttc" id="aclasspcl_1_1_euclidean_cluster_extraction_html_ac4162a11c1fd5797d507068a056bfbf7"><div class="ttname"><a href="classpcl_1_1_euclidean_cluster_extraction.html#ac4162a11c1fd5797d507068a056bfbf7">pcl::EuclideanClusterExtraction::setSearchMethod</a></div><div class="ttdeci">void setSearchMethod(const KdTreePtr &amp;tree)</div><div class="ttdoc">Provide a pointer to the search object.</div><div class="ttdef"><b>Definition:</b> extract_clusters.h:322</div></div>
<div class="ttc" id="aclasspcl_1_1_euclidean_cluster_extraction_html_adb0be906f101b309506cdc37ffd31624"><div class="ttname"><a href="classpcl_1_1_euclidean_cluster_extraction.html#adb0be906f101b309506cdc37ffd31624">pcl::EuclideanClusterExtraction::setMaxClusterSize</a></div><div class="ttdeci">void setMaxClusterSize(int max_cluster_size)</div><div class="ttdoc">Set the maximum number of points that a cluster needs to contain in order to be considered valid.</div><div class="ttdef"><b>Definition:</b> extract_clusters.h:372</div></div>
<div class="ttc" id="aclasspcl_1_1_p_c_l_base_html_a1952d7101f3942bac3b69ed55c1ca7ea"><div class="ttname"><a href="classpcl_1_1_p_c_l_base.html#a1952d7101f3942bac3b69ed55c1ca7ea">pcl::PCLBase::setInputCloud</a></div><div class="ttdeci">virtual void setInputCloud(const PointCloudConstPtr &amp;cloud)</div><div class="ttdoc">Provide a pointer to the input dataset</div><div class="ttdef"><b>Definition:</b> pcl_base.hpp:66</div></div>
<div class="ttc" id="aclasspcl_1_1_p_c_l_base_html_ab219359de6eb34c9d51e2e976dd1a0d1"><div class="ttname"><a href="classpcl_1_1_p_c_l_base.html#ab219359de6eb34c9d51e2e976dd1a0d1">pcl::PCLBase::setIndices</a></div><div class="ttdeci">virtual void setIndices(const IndicesPtr &amp;indices)</div><div class="ttdoc">Provide a pointer to the vector of indices that represents the input data.</div><div class="ttdef"><b>Definition:</b> pcl_base.hpp:73</div></div>
<div class="ttc" id="aclasspcl_1_1_point_cloud_html"><div class="ttname"><a href="classpcl_1_1_point_cloud.html">pcl::PointCloud</a></div><div class="ttdoc">PointCloud represents the base class in PCL for storing collections of 3D points.</div><div class="ttdef"><b>Definition:</b> point_cloud.h:173</div></div>
<div class="ttc" id="aclasspcl_1_1_point_cloud_html_a1155fe4ba5cdc7e83cb72159a4ea02dc"><div class="ttname"><a href="classpcl_1_1_point_cloud.html#a1155fe4ba5cdc7e83cb72159a4ea02dc">pcl::PointCloud::at</a></div><div class="ttdeci">const PointT &amp; at(int column, int row) const</div><div class="ttdoc">Obtain the point given by the (column, row) coordinates. Only works on organized datasets (those that...</div><div class="ttdef"><b>Definition:</b> point_cloud.h:283</div></div>
<div class="ttc" id="aclasspcl_1_1_sample_consensus_model_plane_html"><div class="ttname"><a href="classpcl_1_1_sample_consensus_model_plane.html">pcl::SampleConsensusModelPlane</a></div><div class="ttdoc">SampleConsensusModelPlane defines a model for 3D plane segmentation. The model coefficients are defin...</div><div class="ttdef"><b>Definition:</b> sac_model_plane.h:137</div></div>
<div class="ttc" id="aclasspcl_1_1search_1_1_kd_tree_html"><div class="ttname"><a href="classpcl_1_1search_1_1_kd_tree.html">pcl::search::KdTree</a></div><div class="ttdoc">search::KdTree is a wrapper class which inherits the pcl::KdTree class for performing search function...</div><div class="ttdef"><b>Definition:</b> kdtree.h:63</div></div>
<div class="ttc" id="astructpcl_1_1_point_x_y_z_r_g_b_a_html"><div class="ttname"><a href="structpcl_1_1_point_x_y_z_r_g_b_a.html">pcl::PointXYZRGBA</a></div><div class="ttdoc">A point structure representing Euclidean xyz coordinates, and the RGBA color.</div><div class="ttdef"><b>Definition:</b> point_types.hpp:540</div></div>
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<h2 class="memtitle"><span class="permalink"><a href="#ad918a280410d18af75bad10b3134e5ab">&#9670;&nbsp;</a></span>applyKconvexity()</h2>

<div class="memitem">
<div class="memproto">
<div class="memtemplate">
template&lt;typename PointT &gt; </div>
<table class="mlabels">
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  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">void <a class="el" href="classpcl_1_1_l_c_c_p_segmentation.html">pcl::LCCPSegmentation</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;::applyKconvexity</td>
        </tr>
      </table>
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  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">private</span></span>  </td>
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</div><div class="memdoc">

<p>Connections are only convex if this is true for at least k_arg common neighbors of the two patches. Call <a class="el" href="classpcl_1_1_l_c_c_p_segmentation.html#affa15262ca3f2713ee31412d88697d36">setKFactor</a> before <a class="el" href="classpcl_1_1_c_p_c_segmentation.html#a0ff7ee11473d36cbb774f90de8064908">segment</a> to use this. </p>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">k_arg</td><td>Factor used for extended convexity check </td></tr>
  </table>
  </dd>
</dl>
<div class="fragment"><div class="line"><a name="l00372"></a><span class="lineno">  372</span>&#160;{</div>
<div class="line"><a name="l00373"></a><span class="lineno">  373</span>&#160;  <span class="keywordflow">if</span> (k_arg == 0)</div>
<div class="line"><a name="l00374"></a><span class="lineno">  374</span>&#160;    <span class="keywordflow">return</span>;</div>
<div class="line"><a name="l00375"></a><span class="lineno">  375</span>&#160; </div>
<div class="line"><a name="l00376"></a><span class="lineno">  376</span>&#160;  <span class="keywordtype">bool</span> is_convex;</div>
<div class="line"><a name="l00377"></a><span class="lineno">  377</span>&#160;  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kcount = 0;</div>
<div class="line"><a name="l00378"></a><span class="lineno">  378</span>&#160; </div>
<div class="line"><a name="l00379"></a><span class="lineno">  379</span>&#160;  EdgeIterator edge_itr, edge_itr_end, next_edge;</div>
<div class="line"><a name="l00380"></a><span class="lineno">  380</span>&#160;  boost::tie (edge_itr, edge_itr_end) = boost::edges (<a class="code" href="classpcl_1_1_c_p_c_segmentation.html#adff367bb7eab2ec652da194f36ad2ab4">sv_adjacency_list_</a>);</div>
<div class="line"><a name="l00381"></a><span class="lineno">  381</span>&#160; </div>
<div class="line"><a name="l00382"></a><span class="lineno">  382</span>&#160;  std::pair&lt;OutEdgeIterator, OutEdgeIterator&gt; source_neighbors_range;</div>
<div class="line"><a name="l00383"></a><span class="lineno">  383</span>&#160;  std::pair&lt;OutEdgeIterator, OutEdgeIterator&gt; target_neighbors_range;</div>
<div class="line"><a name="l00384"></a><span class="lineno">  384</span>&#160; </div>
<div class="line"><a name="l00385"></a><span class="lineno">  385</span>&#160;  <span class="comment">// Check all edges in the graph for k-convexity</span></div>
<div class="line"><a name="l00386"></a><span class="lineno">  386</span>&#160;  <span class="keywordflow">for</span> (next_edge = edge_itr; edge_itr != edge_itr_end; edge_itr = next_edge)</div>
<div class="line"><a name="l00387"></a><span class="lineno">  387</span>&#160;  {</div>
<div class="line"><a name="l00388"></a><span class="lineno">  388</span>&#160;    next_edge++;  <span class="comment">// next_edge iterator is neccessary, because removing an edge invalidates the iterator to the current edge</span></div>
<div class="line"><a name="l00389"></a><span class="lineno">  389</span>&#160; </div>
<div class="line"><a name="l00390"></a><span class="lineno">  390</span>&#160;    is_convex = <a class="code" href="classpcl_1_1_c_p_c_segmentation.html#adff367bb7eab2ec652da194f36ad2ab4">sv_adjacency_list_</a>[*edge_itr].is_convex;</div>
<div class="line"><a name="l00391"></a><span class="lineno">  391</span>&#160; </div>
<div class="line"><a name="l00392"></a><span class="lineno">  392</span>&#160;    <span class="keywordflow">if</span> (is_convex)  <span class="comment">// If edge is (0-)convex</span></div>
<div class="line"><a name="l00393"></a><span class="lineno">  393</span>&#160;    {</div>
<div class="line"><a name="l00394"></a><span class="lineno">  394</span>&#160;      kcount = 0;</div>
<div class="line"><a name="l00395"></a><span class="lineno">  395</span>&#160; </div>
<div class="line"><a name="l00396"></a><span class="lineno">  396</span>&#160;      <span class="keyword">const</span> VertexID source = boost::source (*edge_itr, <a class="code" href="classpcl_1_1_c_p_c_segmentation.html#adff367bb7eab2ec652da194f36ad2ab4">sv_adjacency_list_</a>);</div>
<div class="line"><a name="l00397"></a><span class="lineno">  397</span>&#160;      <span class="keyword">const</span> VertexID target = boost::target (*edge_itr, <a class="code" href="classpcl_1_1_c_p_c_segmentation.html#adff367bb7eab2ec652da194f36ad2ab4">sv_adjacency_list_</a>);</div>
<div class="line"><a name="l00398"></a><span class="lineno">  398</span>&#160; </div>
<div class="line"><a name="l00399"></a><span class="lineno">  399</span>&#160;      source_neighbors_range = boost::out_edges (source, <a class="code" href="classpcl_1_1_c_p_c_segmentation.html#adff367bb7eab2ec652da194f36ad2ab4">sv_adjacency_list_</a>);</div>
<div class="line"><a name="l00400"></a><span class="lineno">  400</span>&#160;      target_neighbors_range = boost::out_edges (target, <a class="code" href="classpcl_1_1_c_p_c_segmentation.html#adff367bb7eab2ec652da194f36ad2ab4">sv_adjacency_list_</a>);</div>
<div class="line"><a name="l00401"></a><span class="lineno">  401</span>&#160; </div>
<div class="line"><a name="l00402"></a><span class="lineno">  402</span>&#160;      <span class="comment">// Find common neighbors, check their connection</span></div>
<div class="line"><a name="l00403"></a><span class="lineno">  403</span>&#160;      <span class="keywordflow">for</span> (OutEdgeIterator source_neighbors_itr = source_neighbors_range.first; source_neighbors_itr != source_neighbors_range.second; ++source_neighbors_itr)  <span class="comment">// For all supervoxels</span></div>
<div class="line"><a name="l00404"></a><span class="lineno">  404</span>&#160;      {</div>
<div class="line"><a name="l00405"></a><span class="lineno">  405</span>&#160;        VertexID source_neighbor_ID = boost::target (*source_neighbors_itr, <a class="code" href="classpcl_1_1_c_p_c_segmentation.html#adff367bb7eab2ec652da194f36ad2ab4">sv_adjacency_list_</a>);</div>
<div class="line"><a name="l00406"></a><span class="lineno">  406</span>&#160; </div>
<div class="line"><a name="l00407"></a><span class="lineno">  407</span>&#160;        <span class="keywordflow">for</span> (OutEdgeIterator target_neighbors_itr = target_neighbors_range.first; target_neighbors_itr != target_neighbors_range.second; ++target_neighbors_itr)  <span class="comment">// For all supervoxels</span></div>
<div class="line"><a name="l00408"></a><span class="lineno">  408</span>&#160;        {</div>
<div class="line"><a name="l00409"></a><span class="lineno">  409</span>&#160;          VertexID target_neighbor_ID = boost::target (*target_neighbors_itr, <a class="code" href="classpcl_1_1_c_p_c_segmentation.html#adff367bb7eab2ec652da194f36ad2ab4">sv_adjacency_list_</a>);</div>
<div class="line"><a name="l00410"></a><span class="lineno">  410</span>&#160;          <span class="keywordflow">if</span> (source_neighbor_ID == target_neighbor_ID)  <span class="comment">// Common neighbor</span></div>
<div class="line"><a name="l00411"></a><span class="lineno">  411</span>&#160;          {</div>
<div class="line"><a name="l00412"></a><span class="lineno">  412</span>&#160;            EdgeID src_edge = boost::edge (source, source_neighbor_ID, <a class="code" href="classpcl_1_1_c_p_c_segmentation.html#adff367bb7eab2ec652da194f36ad2ab4">sv_adjacency_list_</a>).first;</div>
<div class="line"><a name="l00413"></a><span class="lineno">  413</span>&#160;            EdgeID tar_edge = boost::edge (target, source_neighbor_ID, <a class="code" href="classpcl_1_1_c_p_c_segmentation.html#adff367bb7eab2ec652da194f36ad2ab4">sv_adjacency_list_</a>).first;</div>
<div class="line"><a name="l00414"></a><span class="lineno">  414</span>&#160; </div>
<div class="line"><a name="l00415"></a><span class="lineno">  415</span>&#160;            <span class="keywordtype">bool</span> src_is_convex = (<a class="code" href="classpcl_1_1_c_p_c_segmentation.html#adff367bb7eab2ec652da194f36ad2ab4">sv_adjacency_list_</a>)[src_edge].is_convex;</div>
<div class="line"><a name="l00416"></a><span class="lineno">  416</span>&#160;            <span class="keywordtype">bool</span> tar_is_convex = (<a class="code" href="classpcl_1_1_c_p_c_segmentation.html#adff367bb7eab2ec652da194f36ad2ab4">sv_adjacency_list_</a>)[tar_edge].is_convex;</div>
<div class="line"><a name="l00417"></a><span class="lineno">  417</span>&#160; </div>
<div class="line"><a name="l00418"></a><span class="lineno">  418</span>&#160;            <span class="keywordflow">if</span> (src_is_convex &amp;&amp; tar_is_convex)</div>
<div class="line"><a name="l00419"></a><span class="lineno">  419</span>&#160;              ++kcount;</div>
<div class="line"><a name="l00420"></a><span class="lineno">  420</span>&#160; </div>
<div class="line"><a name="l00421"></a><span class="lineno">  421</span>&#160;            <span class="keywordflow">break</span>;</div>
<div class="line"><a name="l00422"></a><span class="lineno">  422</span>&#160;          }</div>
<div class="line"><a name="l00423"></a><span class="lineno">  423</span>&#160;        }</div>
<div class="line"><a name="l00424"></a><span class="lineno">  424</span>&#160; </div>
<div class="line"><a name="l00425"></a><span class="lineno">  425</span>&#160;        <span class="keywordflow">if</span> (kcount &gt;= k_arg)  <span class="comment">// Connection is k-convex, stop search</span></div>
<div class="line"><a name="l00426"></a><span class="lineno">  426</span>&#160;          <span class="keywordflow">break</span>;</div>
<div class="line"><a name="l00427"></a><span class="lineno">  427</span>&#160;      }</div>
<div class="line"><a name="l00428"></a><span class="lineno">  428</span>&#160; </div>
<div class="line"><a name="l00429"></a><span class="lineno">  429</span>&#160;      <span class="comment">// Check k convexity</span></div>
<div class="line"><a name="l00430"></a><span class="lineno">  430</span>&#160;      <span class="keywordflow">if</span> (kcount &lt; k_arg)</div>
<div class="line"><a name="l00431"></a><span class="lineno">  431</span>&#160;        (<a class="code" href="classpcl_1_1_c_p_c_segmentation.html#adff367bb7eab2ec652da194f36ad2ab4">sv_adjacency_list_</a>)[*edge_itr].is_valid = <span class="keyword">false</span>;</div>
<div class="line"><a name="l00432"></a><span class="lineno">  432</span>&#160;    }</div>
<div class="line"><a name="l00433"></a><span class="lineno">  433</span>&#160;  }</div>
<div class="line"><a name="l00434"></a><span class="lineno">  434</span>&#160;}</div>
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<h2 class="memtitle"><span class="permalink"><a href="#a64ade0e74f07da2c8100a1a9d5d46e00">&#9670;&nbsp;</a></span>calculateConvexConnections()</h2>

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<div class="memtemplate">
template&lt;typename PointT &gt; </div>
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          <td class="memname">void <a class="el" href="classpcl_1_1_l_c_c_p_segmentation.html">pcl::LCCPSegmentation</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;::calculateConvexConnections</td>
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<p>Calculates convexity of edges and saves this to the adjacency graph. </p>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in,out]</td><td class="paramname">adjacency_list_arg</td><td>The supervoxel adjacency list </td></tr>
  </table>
  </dd>
</dl>
<div class="fragment"><div class="line"><a name="l00438"></a><span class="lineno">  438</span>&#160;{</div>
<div class="line"><a name="l00439"></a><span class="lineno">  439</span>&#160;  <span class="keywordtype">bool</span> is_convex;</div>
<div class="line"><a name="l00440"></a><span class="lineno">  440</span>&#160; </div>
<div class="line"><a name="l00441"></a><span class="lineno">  441</span>&#160;  EdgeIterator edge_itr, edge_itr_end, next_edge;</div>
<div class="line"><a name="l00442"></a><span class="lineno">  442</span>&#160;  boost::tie (edge_itr, edge_itr_end) = boost::edges (adjacency_list_arg);</div>
<div class="line"><a name="l00443"></a><span class="lineno">  443</span>&#160; </div>
<div class="line"><a name="l00444"></a><span class="lineno">  444</span>&#160;  <span class="keywordflow">for</span> (next_edge = edge_itr; edge_itr != edge_itr_end; edge_itr = next_edge)</div>
<div class="line"><a name="l00445"></a><span class="lineno">  445</span>&#160;  {</div>
<div class="line"><a name="l00446"></a><span class="lineno">  446</span>&#160;    next_edge++;  <span class="comment">// next_edge iterator is neccessary, because removing an edge invalidates the iterator to the current edge</span></div>
<div class="line"><a name="l00447"></a><span class="lineno">  447</span>&#160; </div>
<div class="line"><a name="l00448"></a><span class="lineno">  448</span>&#160;    uint32_t source_sv_label = adjacency_list_arg[boost::source (*edge_itr, adjacency_list_arg)];</div>
<div class="line"><a name="l00449"></a><span class="lineno">  449</span>&#160;    uint32_t target_sv_label = adjacency_list_arg[boost::target (*edge_itr, adjacency_list_arg)];</div>
<div class="line"><a name="l00450"></a><span class="lineno">  450</span>&#160; </div>
<div class="line"><a name="l00451"></a><span class="lineno">  451</span>&#160;    <span class="keywordtype">float</span> normal_difference;</div>
<div class="line"><a name="l00452"></a><span class="lineno">  452</span>&#160;    is_convex = <a class="code" href="classpcl_1_1_l_c_c_p_segmentation.html#ab7189d3e7ad6bcb44b995664c6a0b77e">connIsConvex</a> (source_sv_label, target_sv_label, normal_difference);</div>
<div class="line"><a name="l00453"></a><span class="lineno">  453</span>&#160;    adjacency_list_arg[*edge_itr].is_convex = is_convex;</div>
<div class="line"><a name="l00454"></a><span class="lineno">  454</span>&#160;    adjacency_list_arg[*edge_itr].is_valid = is_convex;</div>
<div class="line"><a name="l00455"></a><span class="lineno">  455</span>&#160;    adjacency_list_arg[*edge_itr].normal_difference = normal_difference;</div>
<div class="line"><a name="l00456"></a><span class="lineno">  456</span>&#160;  }</div>
<div class="line"><a name="l00457"></a><span class="lineno">  457</span>&#160;}</div>
<div class="ttc" id="aclasspcl_1_1_l_c_c_p_segmentation_html_ab7189d3e7ad6bcb44b995664c6a0b77e"><div class="ttname"><a href="classpcl_1_1_l_c_c_p_segmentation.html#ab7189d3e7ad6bcb44b995664c6a0b77e">pcl::LCCPSegmentation::connIsConvex</a></div><div class="ttdeci">bool connIsConvex(const uint32_t source_label_arg, const uint32_t target_label_arg, float &amp;normal_angle)</div><div class="ttdoc">Returns true if the connection between source and target is convex.</div><div class="ttdef"><b>Definition:</b> lccp_segmentation.hpp:460</div></div>
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<h2 class="memtitle"><span class="permalink"><a href="#ad1a810ce20594b9a9309c29f089f0d18">&#9670;&nbsp;</a></span>doGrouping()</h2>

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template&lt;typename PointT &gt; </div>
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          <td class="memname">void <a class="el" href="classpcl_1_1_l_c_c_p_segmentation.html">pcl::LCCPSegmentation</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;::doGrouping</td>
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<p>Perform depth search on the graph and recursively group all supervoxels with convex connections </p><dl class="section note"><dt>注解</dt><dd>The vertices in the supervoxel adjacency list are the supervoxel centroids </dd></dl>
<div class="fragment"><div class="line"><a name="l00309"></a><span class="lineno">  309</span>&#160;{</div>
<div class="line"><a name="l00310"></a><span class="lineno">  310</span>&#160;  <span class="comment">// clear the processed_ map</span></div>
<div class="line"><a name="l00311"></a><span class="lineno">  311</span>&#160;  <a class="code" href="classpcl_1_1_l_c_c_p_segmentation.html#af38c9be4e674843ba21de27b73ca0189">seg_label_to_sv_list_map_</a>.clear ();</div>
<div class="line"><a name="l00312"></a><span class="lineno">  312</span>&#160;  <span class="keywordflow">for</span> (<span class="keyword">typename</span> std::map&lt;uint32_t, <span class="keyword">typename</span> pcl::Supervoxel&lt;PointT&gt;::Ptr&gt;::iterator svlabel_itr = <a class="code" href="classpcl_1_1_c_p_c_segmentation.html#a6e8c0fd169543d42903904b02d36239b">sv_label_to_supervoxel_map_</a>.begin ();</div>
<div class="line"><a name="l00313"></a><span class="lineno">  313</span>&#160;      svlabel_itr != <a class="code" href="classpcl_1_1_c_p_c_segmentation.html#a6e8c0fd169543d42903904b02d36239b">sv_label_to_supervoxel_map_</a>.end (); ++svlabel_itr)</div>
<div class="line"><a name="l00314"></a><span class="lineno">  314</span>&#160;  {</div>
<div class="line"><a name="l00315"></a><span class="lineno">  315</span>&#160;    <span class="keyword">const</span> uint32_t&amp; sv_label = svlabel_itr-&gt;first;</div>
<div class="line"><a name="l00316"></a><span class="lineno">  316</span>&#160;    <a class="code" href="classpcl_1_1_l_c_c_p_segmentation.html#a40779a978d7208a82cc9421c4033a1e4">processed_</a>[sv_label] = <span class="keyword">false</span>;</div>
<div class="line"><a name="l00317"></a><span class="lineno">  317</span>&#160;    <a class="code" href="classpcl_1_1_c_p_c_segmentation.html#afb6ff37d270e3f16c69c46560a1fafce">sv_label_to_seg_label_map_</a>[sv_label] = 0;</div>
<div class="line"><a name="l00318"></a><span class="lineno">  318</span>&#160;  }</div>
<div class="line"><a name="l00319"></a><span class="lineno">  319</span>&#160;  </div>
<div class="line"><a name="l00320"></a><span class="lineno">  320</span>&#160;  <span class="comment">// Perform depth search on the graph and recursively group all supervoxels with convex connections</span></div>
<div class="line"><a name="l00321"></a><span class="lineno">  321</span>&#160;  <span class="comment">//The vertices in the supervoxel adjacency list are the supervoxel centroids</span></div>
<div class="line"><a name="l00322"></a><span class="lineno">  322</span>&#160;  std::pair&lt; VertexIterator, VertexIterator&gt; vertex_iterator_range;</div>
<div class="line"><a name="l00323"></a><span class="lineno">  323</span>&#160;  vertex_iterator_range = boost::vertices (<a class="code" href="classpcl_1_1_c_p_c_segmentation.html#adff367bb7eab2ec652da194f36ad2ab4">sv_adjacency_list_</a>);</div>
<div class="line"><a name="l00324"></a><span class="lineno">  324</span>&#160; </div>
<div class="line"><a name="l00325"></a><span class="lineno">  325</span>&#160;  <span class="comment">// Note: *sv_itr is of type &quot; boost::graph_traits&lt;VoxelAdjacencyList&gt;::vertex_descriptor &quot; which it nothing but a typedef of size_t..</span></div>
<div class="line"><a name="l00326"></a><span class="lineno">  326</span>&#160;  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> segment_label = 1;  <span class="comment">// This starts at 1, because 0 is reserved for errors</span></div>
<div class="line"><a name="l00327"></a><span class="lineno">  327</span>&#160;  <span class="keywordflow">for</span> (VertexIterator sv_itr = vertex_iterator_range.first; sv_itr != vertex_iterator_range.second; ++sv_itr)  <span class="comment">// For all supervoxels</span></div>
<div class="line"><a name="l00328"></a><span class="lineno">  328</span>&#160;  {</div>
<div class="line"><a name="l00329"></a><span class="lineno">  329</span>&#160;    <span class="keyword">const</span> VertexID sv_vertex_id = *sv_itr;</div>
<div class="line"><a name="l00330"></a><span class="lineno">  330</span>&#160;    <span class="keyword">const</span> uint32_t&amp; sv_label = <a class="code" href="classpcl_1_1_c_p_c_segmentation.html#adff367bb7eab2ec652da194f36ad2ab4">sv_adjacency_list_</a>[sv_vertex_id];</div>
<div class="line"><a name="l00331"></a><span class="lineno">  331</span>&#160;    <span class="keywordflow">if</span> (!<a class="code" href="classpcl_1_1_l_c_c_p_segmentation.html#a40779a978d7208a82cc9421c4033a1e4">processed_</a>[sv_label])</div>
<div class="line"><a name="l00332"></a><span class="lineno">  332</span>&#160;    {</div>
<div class="line"><a name="l00333"></a><span class="lineno">  333</span>&#160;      <span class="comment">// Add neighbors (and their neighbors etc.) to group if similarity constraint is met</span></div>
<div class="line"><a name="l00334"></a><span class="lineno">  334</span>&#160;      <a class="code" href="classpcl_1_1_l_c_c_p_segmentation.html#a4a492a4362ba841361155407ef78c00e">recursiveSegmentGrowing</a> (sv_vertex_id, segment_label);</div>
<div class="line"><a name="l00335"></a><span class="lineno">  335</span>&#160;      ++segment_label;  <span class="comment">// After recursive grouping ended (no more neighbors to consider) -&gt; go to next group</span></div>
<div class="line"><a name="l00336"></a><span class="lineno">  336</span>&#160;    }</div>
<div class="line"><a name="l00337"></a><span class="lineno">  337</span>&#160;  }</div>
<div class="line"><a name="l00338"></a><span class="lineno">  338</span>&#160;}</div>
<div class="ttc" id="aclasspcl_1_1_l_c_c_p_segmentation_html_a40779a978d7208a82cc9421c4033a1e4"><div class="ttname"><a href="classpcl_1_1_l_c_c_p_segmentation.html#a40779a978d7208a82cc9421c4033a1e4">pcl::LCCPSegmentation::processed_</a></div><div class="ttdeci">std::map&lt; uint32_t, bool &gt; processed_</div><div class="ttdoc">Stores which supervoxel labels were already visited during recursive grouping.</div><div class="ttdef"><b>Definition:</b> lccp_segmentation.h:333</div></div>
<div class="ttc" id="aclasspcl_1_1_l_c_c_p_segmentation_html_a4a492a4362ba841361155407ef78c00e"><div class="ttname"><a href="classpcl_1_1_l_c_c_p_segmentation.html#a4a492a4362ba841361155407ef78c00e">pcl::LCCPSegmentation::recursiveSegmentGrowing</a></div><div class="ttdeci">void recursiveSegmentGrowing(const VertexID &amp;queryPointID, const unsigned int group_label)</div><div class="ttdoc">Assigns neighbors of the query point to the same group as the query point. Recursive part of doGroupi...</div><div class="ttdef"><b>Definition:</b> lccp_segmentation.hpp:341</div></div>
<div class="ttc" id="aclasspcl_1_1_l_c_c_p_segmentation_html_af38c9be4e674843ba21de27b73ca0189"><div class="ttname"><a href="classpcl_1_1_l_c_c_p_segmentation.html#af38c9be4e674843ba21de27b73ca0189">pcl::LCCPSegmentation::seg_label_to_sv_list_map_</a></div><div class="ttdeci">std::map&lt; uint32_t, std::set&lt; uint32_t &gt; &gt; seg_label_to_sv_list_map_</div><div class="ttdoc">map &lt;Segment Label, std::set &lt;SuperVoxel Labels&gt; &gt;</div><div class="ttdef"><b>Definition:</b> lccp_segmentation.h:346</div></div>
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<h2 class="memtitle"><span class="permalink"><a href="#a0ff7ee11473d36cbb774f90de8064908">&#9670;&nbsp;</a></span>segment()</h2>

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          <td class="memname">void <a class="el" href="classpcl_1_1_c_p_c_segmentation.html">pcl::CPCSegmentation</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;::segment</td>
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<p>Merge supervoxels using cuts through local convexities. The input parameters are generated by using the <a class="el" href="classpcl_1_1_supervoxel_clustering.html">SupervoxelClustering</a> class. To retrieve the output use the <a class="el" href="classpcl_1_1_l_c_c_p_segmentation.html#a73b148531688cdb949bb9185e6ada8e9">relabelCloud</a> method. </p>
<dl class="section note"><dt>注解</dt><dd>There are three ways to retrieve the segmentation afterwards (inherited from <a class="el" href="classpcl_1_1_l_c_c_p_segmentation.html">LCCPSegmentation</a>): <a class="el" href="classpcl_1_1_l_c_c_p_segmentation.html#a73b148531688cdb949bb9185e6ada8e9">relabelCloud</a>, getSegmentSupervoxelMap and getSupervoxelSegmentMap </dd></dl>
<div class="fragment"><div class="line"><a name="l00061"></a><span class="lineno">   61</span>&#160;{</div>
<div class="line"><a name="l00062"></a><span class="lineno">   62</span>&#160;  <span class="keywordflow">if</span> (<a class="code" href="classpcl_1_1_c_p_c_segmentation.html#a0ebcf3b12da8ec8ff9029a4bc77292b6">supervoxels_set_</a>)</div>
<div class="line"><a name="l00063"></a><span class="lineno">   63</span>&#160;  {</div>
<div class="line"><a name="l00064"></a><span class="lineno">   64</span>&#160;  <span class="comment">// Calculate for every Edge if the connection is convex or invalid</span></div>
<div class="line"><a name="l00065"></a><span class="lineno">   65</span>&#160;  <span class="comment">// This effectively performs the segmentation.</span></div>
<div class="line"><a name="l00066"></a><span class="lineno">   66</span>&#160;    <a class="code" href="classpcl_1_1_c_p_c_segmentation.html#a64ade0e74f07da2c8100a1a9d5d46e00">calculateConvexConnections</a> (<a class="code" href="classpcl_1_1_c_p_c_segmentation.html#adff367bb7eab2ec652da194f36ad2ab4">sv_adjacency_list_</a>);</div>
<div class="line"><a name="l00067"></a><span class="lineno">   67</span>&#160; </div>
<div class="line"><a name="l00068"></a><span class="lineno">   68</span>&#160;    <span class="comment">// Correct edge relations using extended convexity definition if k&gt;0</span></div>
<div class="line"><a name="l00069"></a><span class="lineno">   69</span>&#160;    <a class="code" href="classpcl_1_1_c_p_c_segmentation.html#ad918a280410d18af75bad10b3134e5ab">applyKconvexity</a> (<a class="code" href="classpcl_1_1_c_p_c_segmentation.html#ab56b15cb177706d688e6773368e123e2">k_factor_</a>);</div>
<div class="line"><a name="l00070"></a><span class="lineno">   70</span>&#160; </div>
<div class="line"><a name="l00071"></a><span class="lineno">   71</span>&#160;    <span class="comment">// Determine wether to use cutting planes</span></div>
<div class="line"><a name="l00072"></a><span class="lineno">   72</span>&#160;    <a class="code" href="classpcl_1_1_c_p_c_segmentation.html#ad1a810ce20594b9a9309c29f089f0d18">doGrouping</a> ();</div>
<div class="line"><a name="l00073"></a><span class="lineno">   73</span>&#160; </div>
<div class="line"><a name="l00074"></a><span class="lineno">   74</span>&#160;    <a class="code" href="classpcl_1_1_c_p_c_segmentation.html#a428e19cb5f6711c7d2e20f31472a876a">grouping_data_valid_</a> = <span class="keyword">true</span>;</div>
<div class="line"><a name="l00075"></a><span class="lineno">   75</span>&#160; </div>
<div class="line"><a name="l00076"></a><span class="lineno">   76</span>&#160;    <a class="code" href="classpcl_1_1_c_p_c_segmentation.html#aba7a4f7d9481b0c9c88edc6d301964d9">applyCuttingPlane</a> (<a class="code" href="classpcl_1_1_c_p_c_segmentation.html#a6293e15b7d22fbb19d7819bedba683f1">max_cuts_</a>);</div>
<div class="line"><a name="l00077"></a><span class="lineno">   77</span>&#160;    </div>
<div class="line"><a name="l00078"></a><span class="lineno">   78</span>&#160;    <span class="comment">// merge small segments</span></div>
<div class="line"><a name="l00079"></a><span class="lineno">   79</span>&#160;    <a class="code" href="classpcl_1_1_c_p_c_segmentation.html#a7f0ada4d9a4918d9dbb9e33e32b23d46">mergeSmallSegments</a> ();</div>
<div class="line"><a name="l00080"></a><span class="lineno">   80</span>&#160;  }</div>
<div class="line"><a name="l00081"></a><span class="lineno">   81</span>&#160;  <span class="keywordflow">else</span></div>
<div class="line"><a name="l00082"></a><span class="lineno">   82</span>&#160;    PCL_WARN (<span class="stringliteral">&quot;[pcl::CPCSegmentation::segment] WARNING: Call function setInputSupervoxels first. Nothing has been done. \n&quot;</span>);</div>
<div class="line"><a name="l00083"></a><span class="lineno">   83</span>&#160;}</div>
<div class="ttc" id="aclasspcl_1_1_c_p_c_segmentation_html_a0ebcf3b12da8ec8ff9029a4bc77292b6"><div class="ttname"><a href="classpcl_1_1_c_p_c_segmentation.html#a0ebcf3b12da8ec8ff9029a4bc77292b6">pcl::CPCSegmentation::supervoxels_set_</a></div><div class="ttdeci">bool supervoxels_set_</div><div class="ttdoc">Marks if supervoxels have been set by calling setInputSupervoxels</div><div class="ttdef"><b>Definition:</b> lccp_segmentation.h:308</div></div>
<div class="ttc" id="aclasspcl_1_1_c_p_c_segmentation_html_a428e19cb5f6711c7d2e20f31472a876a"><div class="ttname"><a href="classpcl_1_1_c_p_c_segmentation.html#a428e19cb5f6711c7d2e20f31472a876a">pcl::CPCSegmentation::grouping_data_valid_</a></div><div class="ttdeci">bool grouping_data_valid_</div><div class="ttdoc">Marks if valid grouping data (sv_adjacency_list_, sv_label_to_seg_label_map_, processed_) is avaiable</div><div class="ttdef"><b>Definition:</b> lccp_segmentation.h:305</div></div>
<div class="ttc" id="aclasspcl_1_1_c_p_c_segmentation_html_a64ade0e74f07da2c8100a1a9d5d46e00"><div class="ttname"><a href="classpcl_1_1_c_p_c_segmentation.html#a64ade0e74f07da2c8100a1a9d5d46e00">pcl::CPCSegmentation::calculateConvexConnections</a></div><div class="ttdeci">void calculateConvexConnections(SupervoxelAdjacencyList &amp;adjacency_list_arg)</div><div class="ttdoc">Calculates convexity of edges and saves this to the adjacency graph.</div><div class="ttdef"><b>Definition:</b> lccp_segmentation.hpp:437</div></div>
<div class="ttc" id="aclasspcl_1_1_c_p_c_segmentation_html_a7f0ada4d9a4918d9dbb9e33e32b23d46"><div class="ttname"><a href="classpcl_1_1_c_p_c_segmentation.html#a7f0ada4d9a4918d9dbb9e33e32b23d46">pcl::CPCSegmentation::mergeSmallSegments</a></div><div class="ttdeci">void mergeSmallSegments()</div><div class="ttdoc">Segments smaller than min_segment_size_ are merged to the label of largest neighbor</div><div class="ttdef"><b>Definition:</b> lccp_segmentation.hpp:171</div></div>
<div class="ttc" id="aclasspcl_1_1_c_p_c_segmentation_html_ab56b15cb177706d688e6773368e123e2"><div class="ttname"><a href="classpcl_1_1_c_p_c_segmentation.html#ab56b15cb177706d688e6773368e123e2">pcl::CPCSegmentation::k_factor_</a></div><div class="ttdeci">uint32_t k_factor_</div><div class="ttdoc">Factor used for k-convexity</div><div class="ttdef"><b>Definition:</b> lccp_segmentation.h:326</div></div>
<div class="ttc" id="aclasspcl_1_1_c_p_c_segmentation_html_ad918a280410d18af75bad10b3134e5ab"><div class="ttname"><a href="classpcl_1_1_c_p_c_segmentation.html#ad918a280410d18af75bad10b3134e5ab">pcl::CPCSegmentation::applyKconvexity</a></div><div class="ttdeci">void applyKconvexity(const unsigned int k_arg)</div><div class="ttdoc">Connections are only convex if this is true for at least k_arg common neighbors of the two patches....</div><div class="ttdef"><b>Definition:</b> lccp_segmentation.hpp:371</div></div>
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<h2 class="memtitle"><span class="permalink"><a href="#a0fdcebc606820bc008e779230503da04">&#9670;&nbsp;</a></span>setCutting()</h2>

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<p>Determines if we want to use cutting planes </p>
<dl class="params"><dt>参数</dt><dd>
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    <tr><td class="paramdir">[in]</td><td class="paramname">max_cuts</td><td>Maximum number of cuts </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">cutting_min_segments</td><td>Minimum segment size for cutting </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">cutting_min_score</td><td>Minimum score a proposed cut has to achieve for being performed </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">locally_constrained</td><td>Decide if we constrain our cuts locally </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">directed_cutting</td><td>Decide if we prefer cuts perpendicular to the edge-direction </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">clean_cutting</td><td>Decide if we cut only edges with supervoxels on opposite sides of the plane (clean) or all edges within the seed_resolution_ distance to the plane (not clean). The later was used in the paper. </td></tr>
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<div class="fragment"><div class="line"><a name="l00115"></a><span class="lineno">  115</span>&#160;      {</div>
<div class="line"><a name="l00116"></a><span class="lineno">  116</span>&#160;        <a class="code" href="classpcl_1_1_c_p_c_segmentation.html#a6293e15b7d22fbb19d7819bedba683f1">max_cuts_</a> = max_cuts;</div>
<div class="line"><a name="l00117"></a><span class="lineno">  117</span>&#160;        <a class="code" href="classpcl_1_1_c_p_c_segmentation.html#a6eda4c6ab0c6d0f55b11c5a666accd7f">min_segment_size_for_cutting_</a> = cutting_min_segments;</div>
<div class="line"><a name="l00118"></a><span class="lineno">  118</span>&#160;        <a class="code" href="classpcl_1_1_c_p_c_segmentation.html#ac04198491da197fdbb9478dbf682f1ec">min_cut_score_</a> = cutting_min_score;</div>
<div class="line"><a name="l00119"></a><span class="lineno">  119</span>&#160;        <a class="code" href="classpcl_1_1_c_p_c_segmentation.html#a6016df56b09c1f9b13db954e1805d930">use_local_constrains_</a> = locally_constrained;</div>
<div class="line"><a name="l00120"></a><span class="lineno">  120</span>&#160;        <a class="code" href="classpcl_1_1_c_p_c_segmentation.html#aceaa9f8130856b9304830674ac7515c8">use_directed_weights_</a> = directed_cutting;</div>
<div class="line"><a name="l00121"></a><span class="lineno">  121</span>&#160;        <a class="code" href="classpcl_1_1_c_p_c_segmentation.html#a743b8b7bfcb33d9edfcbd9fa1ecc2eac">use_clean_cutting_</a> = clean_cutting;</div>
<div class="line"><a name="l00122"></a><span class="lineno">  122</span>&#160;      }</div>
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<h2 class="memtitle"><span class="permalink"><a href="#ae45407cfb0975dc3ab6bb1f77c6df512">&#9670;&nbsp;</a></span>setRANSACIterations()</h2>

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<p>Set the number of iterations for the weighted RANSAC step (best cut estimations) </p>
<dl class="params"><dt>参数</dt><dd>
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    <tr><td class="paramdir">[in]</td><td class="paramname">ransac_iterations</td><td>The number of iterations </td></tr>
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<div class="fragment"><div class="line"><a name="l00128"></a><span class="lineno">  128</span>&#160;      {</div>
<div class="line"><a name="l00129"></a><span class="lineno">  129</span>&#160;        <a class="code" href="classpcl_1_1_c_p_c_segmentation.html#aa1eac80686a308fdd04592bdefd9e6bd">ransac_itrs_</a> = ransac_iterations;</div>
<div class="line"><a name="l00130"></a><span class="lineno">  130</span>&#160;      }</div>
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<h2 class="groupheader">类成员变量说明</h2>
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<h2 class="memtitle"><span class="permalink"><a href="#a90f2ad90bee047f31f2c9ad4f3b0c158">&#9670;&nbsp;</a></span>concavity_tolerance_threshold_</h2>

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          <td class="memname">float <a class="el" href="classpcl_1_1_l_c_c_p_segmentation.html">pcl::LCCPSegmentation</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;::concavity_tolerance_threshold_</td>
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<p>*** Parameters *** /// </p>
<p><a class="el" href="structpcl_1_1_normal.html" title="A point structure representing normal coordinates and the surface curvature estimate....">Normal</a> Threshold in degrees [0,180] used for merging </p>

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<h2 class="memtitle"><span class="permalink"><a href="#a6293e15b7d22fbb19d7819bedba683f1">&#9670;&nbsp;</a></span>max_cuts_</h2>

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<p>*** Parameters *** /// </p>
<p>Maximum number of cuts </p>

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<h2 class="memtitle"><span class="permalink"><a href="#afb6ff37d270e3f16c69c46560a1fafce">&#9670;&nbsp;</a></span>sv_label_to_seg_label_map_</h2>

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          <td class="memname">std::map&lt;uint32_t, uint32_t&gt; <a class="el" href="classpcl_1_1_l_c_c_p_segmentation.html">pcl::LCCPSegmentation</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;::sv_label_to_seg_label_map_</td>
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<p>Storing relation between original SuperVoxel Labels and new segmantion labels. </p>
<dl class="section note"><dt>注解</dt><dd>sv_label_to_seg_label_map_[old_labelID] = new_labelID </dd></dl>

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<hr/>该类的文档由以下文件生成:<ul>
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    <li class="navelem"><b>pcl</b></li><li class="navelem"><a class="el" href="classpcl_1_1_c_p_c_segmentation.html">CPCSegmentation</a></li>
    <li class="footer">制作者 <a href="https://www.doxygen.org/index.html"><img class="footer" src="doxygen.svg" width="104" height="31" alt="doxygen"/></a> 1.9.1 </li>
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